Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2018
Doctoral thesis
Open Access
A Portable, Intelligent, Customizable Device for Human Breath Analysis
Germanese D.
Breath analysis allows for monitoring the metabolic processes that occur in human body in a non-invasive way. Comparing with other traditional methods such as blood test, breath analysis is harmless to not only the subjects but also the personnel who collect the samples. However, despite its great potential, only few breath tests are commonly used in clinical practice nowadays. Breath analysis has not gained a wider use yet. One of the main reasons is related to standard instrumentation for gas analysis. Standard instrumentation, such as gas chromatography, is expensive and time consuming. Its use, as well as the interpretation of the results, often requires specialized personnel. E-nose systems, based on gas sensor array, are easier to use and able to analyze gases in real time, but, although cheaper than a gas chromatograph, their cost remains high. During my research activity, carried on at the Signals and Images Laboratory (SiLab) of the Institute of Information Science and Technology (ISTI) of the National Research Council (CNR), I design and developed the so called Wize Sniffer (WS), a device able to accurately analyze human breath composition and, at the same time, to overcome the limitations of existing instrumentation for gas analysis. The idea of the Wize Sniffer was born in the framework of SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self-monitoring (SE- MEOTICONS, www.semeoticons.eu) European Project, and it was designed for detecting, in human breath, those molecules related to the noxious habits for cardio-metabolic risk. The clinical assumption behind the Wize Sniffer lied in the fact that harmful habits such as alcohol consumption, smoking, unhealthy diet cause a variation in the concentration of a set of molecules (among which carbon monoxide, ethanol, hydrogen, hydrogen sulfide) in the exhaled breath. Therefore, the goal was to realize a portable and easy-to-use device, based on cheap electronics, to be used by anybody at their home. The main contributions of my work were the following: o design and development of a portable, low cost, customizable, easy to use device, able to be used in whichever context of use: I succeeded in this with using cheap commercial discrete gas sensors and an Arduino board, wrote the software and calibrated the system; o development of a method to analyze breath composition and understand individual's cardio-metabolic risk; I also validated it with success on real people. Given such good outcomes, I wanted the Wize Sniffer took a further step forward, towards the diagnosis in particular. The application field regarded the chronic liver impairment, as the studies which involve e-nose systems in the identification of liver disease are still few. In addition, the diagnosis of liver impairment often requires very invasive clinical test (biopsy, for instance). In this proof-of-concept study, the Wize Sniffer showed good diagnosis-oriented properties in discriminating the severity of liver disease (absence of disease, chronic liver disease, cirrhosis, hepatic encephalopathy) on the base of the detected ammonia.Project(s): SEMEOTICONS
Germanese D.
Breath analysis allows for monitoring the metabolic processes that occur in human body in a non-invasive way. Comparing with other traditional methods such as blood test, breath analysis is harmless to not only the subjects but also the personnel who collect the samples. However, despite its great potential, only few breath tests are commonly used in clinical practice nowadays. Breath analysis has not gained a wider use yet. One of the main reasons is related to standard instrumentation for gas analysis. Standard instrumentation, such as gas chromatography, is expensive and time consuming. Its use, as well as the interpretation of the results, often requires specialized personnel. E-nose systems, based on gas sensor array, are easier to use and able to analyze gases in real time, but, although cheaper than a gas chromatograph, their cost remains high. During my research activity, carried on at the Signals and Images Laboratory (SiLab) of the Institute of Information Science and Technology (ISTI) of the National Research Council (CNR), I design and developed the so called Wize Sniffer (WS), a device able to accurately analyze human breath composition and, at the same time, to overcome the limitations of existing instrumentation for gas analysis. The idea of the Wize Sniffer was born in the framework of SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self-monitoring (SE- MEOTICONS, www.semeoticons.eu) European Project, and it was designed for detecting, in human breath, those molecules related to the noxious habits for cardio-metabolic risk. The clinical assumption behind the Wize Sniffer lied in the fact that harmful habits such as alcohol consumption, smoking, unhealthy diet cause a variation in the concentration of a set of molecules (among which carbon monoxide, ethanol, hydrogen, hydrogen sulfide) in the exhaled breath. Therefore, the goal was to realize a portable and easy-to-use device, based on cheap electronics, to be used by anybody at their home. The main contributions of my work were the following: o design and development of a portable, low cost, customizable, easy to use device, able to be used in whichever context of use: I succeeded in this with using cheap commercial discrete gas sensors and an Arduino board, wrote the software and calibrated the system; o development of a method to analyze breath composition and understand individual's cardio-metabolic risk; I also validated it with success on real people. Given such good outcomes, I wanted the Wize Sniffer took a further step forward, towards the diagnosis in particular. The application field regarded the chronic liver impairment, as the studies which involve e-nose systems in the identification of liver disease are still few. In addition, the diagnosis of liver impairment often requires very invasive clinical test (biopsy, for instance). In this proof-of-concept study, the Wize Sniffer showed good diagnosis-oriented properties in discriminating the severity of liver disease (absence of disease, chronic liver disease, cirrhosis, hepatic encephalopathy) on the base of the detected ammonia.Project(s): SEMEOTICONS
See at:
ISTI Repository 
| CNR ExploRA
2014
Report
Unknown
Wize Sniffer 1.0: development of a new portable device designed for selective olfaction
D'Acunto M., Germanese D.
Digital semeiotics is one of the newest recent challenges for assessing a number of computational descriptors to atherosclerotic cardiovascular diseases that are leading causes of mortality worldwide. These descriptors can be expressed involving (i) morphometric, biometrics and colorimetric of the face; (ii) spectroscopic analysis of skin and iris, of sub-cutaneous substances and the function of subcutaneous tissues, and (iii) compositional analysis of breath and exhaled. In this paper, we describe the design and functionality of the Wize Sniffer (WS), a new portable device for breath analysis limited to an effective number of substances. Within the SEMEOTICON Project by the WS, we intend a hardware/software tool for both the analysis of volatile organic compounds of breath and a platform for data mining and data integration. The WS should be able to provide useful information about the "breathprint", i.e., the analog of fingerprint for the state of health of an individual.Source: ISTI Technical reports, 2014
Project(s): SEMEOTICONS
D'Acunto M., Germanese D.
Digital semeiotics is one of the newest recent challenges for assessing a number of computational descriptors to atherosclerotic cardiovascular diseases that are leading causes of mortality worldwide. These descriptors can be expressed involving (i) morphometric, biometrics and colorimetric of the face; (ii) spectroscopic analysis of skin and iris, of sub-cutaneous substances and the function of subcutaneous tissues, and (iii) compositional analysis of breath and exhaled. In this paper, we describe the design and functionality of the Wize Sniffer (WS), a new portable device for breath analysis limited to an effective number of substances. Within the SEMEOTICON Project by the WS, we intend a hardware/software tool for both the analysis of volatile organic compounds of breath and a platform for data mining and data integration. The WS should be able to provide useful information about the "breathprint", i.e., the analog of fingerprint for the state of health of an individual.Source: ISTI Technical reports, 2014
Project(s): SEMEOTICONS
See at: CNR ExploRA
2016
Conference article
Open Access
Design of a breath analysis device for self-monitoring and remote health-care
Germanese D., D'Acunto M., Salvetti O.
Technique as new as promising, breath analysis enables the monitoring of biochemical processes in human body in a non-invasive way. This is why it is drawing, more and more, the attention of scientific community: many studies have been addressed in order to find a correlation between breath volatile organic compounds (VOCs) and several diseases. Despite its potential, breath analysis is still far from being used in clinical practice. These are some of the principal reasons: (i)high costs for the standard analytical instrumentation; (ii)need of specialized personnel for the interpretation of the results; (iii)lack of standardized procedures to collect breath samples. Our aim is to develop a device, which we call Wize Sniffer (WS), based on commercial gas sensors, which is: (i)able to analyse breath gases in real time; (ii)portable; (iii)low-cost; (iv)easy-to-use also for non-specialized personnel. Another aim is to foster homecare, that means promote the purchase and the use, also in home environment, of such device. The Wize Sniffer is composed of three modules: signal measurement, signal conditioning and signal processing. To satisfy the goal of developing a device by using low-cost technology, its core is composed of an array of commercial, low cost, semiconductor-based gas sensors, and a widely employed open source controller: an Arduino board. To promote the use of such device also in home environment, and foster its daily use, it is programmed in order to send breath test results also to a remote pc: the pc of user's physician, for example. In addition, the design of the Wize Sniffer is based on a modular configuration, thus enabling to change the type of the gas sensors according to the breath molecules to be detected. In this case, we focus our attention to the prevention of cardio-metabolic risk, for which the healthcare systems are registering an exponential growth of social costs, by monitoring those dangerous habits for cardio-metabolic risk itself.Source: BIOSTEC 2016 - 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Doctoral Consortium, pp. 9–14, Roma, Italia, 21-23 Febbraio 2016
Germanese D., D'Acunto M., Salvetti O.
Technique as new as promising, breath analysis enables the monitoring of biochemical processes in human body in a non-invasive way. This is why it is drawing, more and more, the attention of scientific community: many studies have been addressed in order to find a correlation between breath volatile organic compounds (VOCs) and several diseases. Despite its potential, breath analysis is still far from being used in clinical practice. These are some of the principal reasons: (i)high costs for the standard analytical instrumentation; (ii)need of specialized personnel for the interpretation of the results; (iii)lack of standardized procedures to collect breath samples. Our aim is to develop a device, which we call Wize Sniffer (WS), based on commercial gas sensors, which is: (i)able to analyse breath gases in real time; (ii)portable; (iii)low-cost; (iv)easy-to-use also for non-specialized personnel. Another aim is to foster homecare, that means promote the purchase and the use, also in home environment, of such device. The Wize Sniffer is composed of three modules: signal measurement, signal conditioning and signal processing. To satisfy the goal of developing a device by using low-cost technology, its core is composed of an array of commercial, low cost, semiconductor-based gas sensors, and a widely employed open source controller: an Arduino board. To promote the use of such device also in home environment, and foster its daily use, it is programmed in order to send breath test results also to a remote pc: the pc of user's physician, for example. In addition, the design of the Wize Sniffer is based on a modular configuration, thus enabling to change the type of the gas sensors according to the breath molecules to be detected. In this case, we focus our attention to the prevention of cardio-metabolic risk, for which the healthcare systems are registering an exponential growth of social costs, by monitoring those dangerous habits for cardio-metabolic risk itself.Source: BIOSTEC 2016 - 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Doctoral Consortium, pp. 9–14, Roma, Italia, 21-23 Febbraio 2016
See at:
ISTI Repository | CNR ExploRA
2016
Report
Unknown
SEMEOTICONS - Software Integration and Wize Mirror user manual
Martinelli M., Germanese D., Pascali M. A., Mazzarisi A., Henriquez P., Vitali I.
This deliverable reports the results of software integration of the WM prototype and includes the User Manual for the final prototype of the Wize Mirror (WM) as finalized during the last software integration phase that has set up the complete system (see D8.5.1).Source: Project report, SEMEOTICONS, Deliverable D8.5.2, 2016
Project(s): SEMEOTICONS
Martinelli M., Germanese D., Pascali M. A., Mazzarisi A., Henriquez P., Vitali I.
This deliverable reports the results of software integration of the WM prototype and includes the User Manual for the final prototype of the Wize Mirror (WM) as finalized during the last software integration phase that has set up the complete system (see D8.5.1).Source: Project report, SEMEOTICONS, Deliverable D8.5.2, 2016
Project(s): SEMEOTICONS
See at: CNR ExploRA
2019
Journal article
Open Access
An E-Nose for the Monitoring of Severe Liver Impairment: A Preliminary Study
Germanese D., Colantonio S., D'Acunto M., Romagnoli V., Salvati A., Brunetto M.
Biologically inspired to mammalian olfactory system, electronic noses became popular during the last three decades. In literature, as well as in daily practice, a wide range of applications are reported. Nevertheless, the most pioneering one has been (and still is) the assessment of the human breath composition. In this study, we used a prototype of electronic nose, called Wize Sniffer (WS) and based it on an array of semiconductor gas sensor, to detect ammonia in the breath of patients suffering from severe liver impairment. In the setting of severely impaired liver, toxic substances, such as ammonia, accumulate in the systemic circulation and in the brain. This may result in Hepatic Encephalopathy (HE), a spectrum of neuro-psychiatric abnormalities which include changes in cognitive functions, consciousness, and behaviour. HE can be detected only by specific but time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. In the presented proof-of-concept study, we aimed at investigating the possibility of discriminating the severity degree of liver impairment on the basis of the detected breath ammonia, in view of the detection of HE at its early stage.Source: Sensors (Basel) 19 (2019). doi:10.3390/s19173656
DOI: 10.3390/s19173656
Metrics:
Germanese D., Colantonio S., D'Acunto M., Romagnoli V., Salvati A., Brunetto M.
Biologically inspired to mammalian olfactory system, electronic noses became popular during the last three decades. In literature, as well as in daily practice, a wide range of applications are reported. Nevertheless, the most pioneering one has been (and still is) the assessment of the human breath composition. In this study, we used a prototype of electronic nose, called Wize Sniffer (WS) and based it on an array of semiconductor gas sensor, to detect ammonia in the breath of patients suffering from severe liver impairment. In the setting of severely impaired liver, toxic substances, such as ammonia, accumulate in the systemic circulation and in the brain. This may result in Hepatic Encephalopathy (HE), a spectrum of neuro-psychiatric abnormalities which include changes in cognitive functions, consciousness, and behaviour. HE can be detected only by specific but time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. In the presented proof-of-concept study, we aimed at investigating the possibility of discriminating the severity degree of liver impairment on the basis of the detected breath ammonia, in view of the detection of HE at its early stage.Source: Sensors (Basel) 19 (2019). doi:10.3390/s19173656
DOI: 10.3390/s19173656
Metrics:
See at:
Sensors | Sensors | ISTI Repository | Sensors | Sensors | CNR ExploRA
2019
Conference article
Open Access
Towards chronic liver dysfunction self-monitoring: a proof-of-concept study
Germanese D., Colantonio S., D'Acunto M., Brunetto M., Romagnoli V., Salvati A.
The liver is our very own chemical processing plant as it plays a vital role in maintaining the body's metabolic balance. Liver's health is assessed by a group of clinical tests (such as blood tests, ultrasonographic imaging, liver biopsy) most of which are invasive and burdensome for the patients. In the setting of severely scarred liver, toxic substances, such as ammonia, have fewer opportunities to be detoxified. Accumulation of ammonia in the systemic circulation and in the brain may result in Hepatic Encephalopathy (HE), a spectrum of neuropsychiatric abnormalities which entails changes in consciousness, intellectual functions, behavior. Minimal HE has attracted increasing attention, as it does not cause detectable changes in personality or behaviour, but the complex and sustained attention is impaired. Hence, it can be detected only by specific but biased, time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. The obstrusivity of the majority of the liver function clinical tests, and, in case of minimal HE, the lack of reliable examinations, are encouraging the scientific community to look for alternative diagnostic methods. For this purpose, the exploitation of a non-invasive technique such as breath analysis, to identify chronic liver disease, discriminate among its degree of severity and detect the onset of HE, could be a step forward for clinical diagnosis. In this paper, we report a proof-of-concept study that aimed at detecting ammonia in the breath of patients suffering from chronic liver disease by means of a low-cost, easy-to-use, gas-sensors based device. Not only, we also aimed at investigating the possibility of discriminating the several severity degree of liver impairment on the basis of the detected ammonia.Source: ISCC 2019 - IEEE Symposium on Computers and Communications, Barcelona, Spain, 29 June - 3 July, 2019
DOI: 10.1109/iscc47284.2019.8969605
Metrics:
Germanese D., Colantonio S., D'Acunto M., Brunetto M., Romagnoli V., Salvati A.
The liver is our very own chemical processing plant as it plays a vital role in maintaining the body's metabolic balance. Liver's health is assessed by a group of clinical tests (such as blood tests, ultrasonographic imaging, liver biopsy) most of which are invasive and burdensome for the patients. In the setting of severely scarred liver, toxic substances, such as ammonia, have fewer opportunities to be detoxified. Accumulation of ammonia in the systemic circulation and in the brain may result in Hepatic Encephalopathy (HE), a spectrum of neuropsychiatric abnormalities which entails changes in consciousness, intellectual functions, behavior. Minimal HE has attracted increasing attention, as it does not cause detectable changes in personality or behaviour, but the complex and sustained attention is impaired. Hence, it can be detected only by specific but biased, time-consuming and burdensome examinations, such as blood ammonia levels assessment and neuro-psychological tests. The obstrusivity of the majority of the liver function clinical tests, and, in case of minimal HE, the lack of reliable examinations, are encouraging the scientific community to look for alternative diagnostic methods. For this purpose, the exploitation of a non-invasive technique such as breath analysis, to identify chronic liver disease, discriminate among its degree of severity and detect the onset of HE, could be a step forward for clinical diagnosis. In this paper, we report a proof-of-concept study that aimed at detecting ammonia in the breath of patients suffering from chronic liver disease by means of a low-cost, easy-to-use, gas-sensors based device. Not only, we also aimed at investigating the possibility of discriminating the several severity degree of liver impairment on the basis of the detected ammonia.Source: ISCC 2019 - IEEE Symposium on Computers and Communications, Barcelona, Spain, 29 June - 3 July, 2019
DOI: 10.1109/iscc47284.2019.8969605
Metrics:
See at:
ISTI Repository | doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2020
Report
Open Access
Analisi di immagini tomografiche ad alta risoluzione attraverso reti neurali convoluzionali per lo studio delle interstiziopatie polmonari
Buongiorno R., Colantonio S., Germanese D.
Le interstiziopatie polmonari (Interstitial Lung Disease, ILD) sono patologie croniche che causano la cicatrizzazione del parenchima polmonare e dell'interstizio alveolare e la compromissione della funzionalità respiratoria. Dal momento che sono più di 200 le patologie raggruppate nella categoria delle ILD, una precisa identificazione è fondamentale per individuare la terapia migliore e formulare una prognosi. L'esame radiologico di riferimento è la tomografia computerizzata del torace ad alta risoluzione (High Resolution Computed Tomography, HRCT) e rappresenta un passaggio cruciale nel processo di diagnosi; nell'analizzare le immagini, infatti, il radiologo deve stabilire se vi è Usual Interstitial Pneumoniae (UIP), ovvero presenza di pattern istopatologici tipici della malattia, e valutarne l'estensione, correlata con la gravità delle alterazioni fisiologiche. Tuttavia, l'incidenza rara delle interstiziopatie fa sì che non tutti i radiologi abbiano un grado di esperienza adatto a individuare visivamente l'anomalia. Inoltre, la malattia si diffonde lungo tutti i polmoni e la segmentazione manuale risulta faticosa. Nel tentativo di rimediare alla variabilità intra- ed inter-osservatore, sono state sviluppate tecniche per il riconoscimento automatico dei pattern UIP; vi sono approcci basati sull'analisi dell'istogramma e della texture dell'immagine ma, dal momento che i classificatori sono stati addestrati su label definite da operatori clinici diversi, presentano comunque un bias che è causa di identificazioni errate, o mancate, dei pattern. Il deep learning, invece, si distingue dalle tecniche tradizionali perché fornisce strumenti che imparano autonomamente a classificare i dati. L'obiettivo del lavoro è stato, quindi, progettare e sviluppare la UIP-net, una rete neurale convoluzionale ad-hoc per la segmentazione automatica dei pattern UIP in immagini HRCT di pazienti con Fibrosi Idiopatica Polmonare (IPF), che è una sotto-categoria delle ILD.Source: ISTI Technical Reports 007/2020, 2020, 2020
DOI: 10.32079/isti-tr-2020/007
Metrics:
Buongiorno R., Colantonio S., Germanese D.
Le interstiziopatie polmonari (Interstitial Lung Disease, ILD) sono patologie croniche che causano la cicatrizzazione del parenchima polmonare e dell'interstizio alveolare e la compromissione della funzionalità respiratoria. Dal momento che sono più di 200 le patologie raggruppate nella categoria delle ILD, una precisa identificazione è fondamentale per individuare la terapia migliore e formulare una prognosi. L'esame radiologico di riferimento è la tomografia computerizzata del torace ad alta risoluzione (High Resolution Computed Tomography, HRCT) e rappresenta un passaggio cruciale nel processo di diagnosi; nell'analizzare le immagini, infatti, il radiologo deve stabilire se vi è Usual Interstitial Pneumoniae (UIP), ovvero presenza di pattern istopatologici tipici della malattia, e valutarne l'estensione, correlata con la gravità delle alterazioni fisiologiche. Tuttavia, l'incidenza rara delle interstiziopatie fa sì che non tutti i radiologi abbiano un grado di esperienza adatto a individuare visivamente l'anomalia. Inoltre, la malattia si diffonde lungo tutti i polmoni e la segmentazione manuale risulta faticosa. Nel tentativo di rimediare alla variabilità intra- ed inter-osservatore, sono state sviluppate tecniche per il riconoscimento automatico dei pattern UIP; vi sono approcci basati sull'analisi dell'istogramma e della texture dell'immagine ma, dal momento che i classificatori sono stati addestrati su label definite da operatori clinici diversi, presentano comunque un bias che è causa di identificazioni errate, o mancate, dei pattern. Il deep learning, invece, si distingue dalle tecniche tradizionali perché fornisce strumenti che imparano autonomamente a classificare i dati. L'obiettivo del lavoro è stato, quindi, progettare e sviluppare la UIP-net, una rete neurale convoluzionale ad-hoc per la segmentazione automatica dei pattern UIP in immagini HRCT di pazienti con Fibrosi Idiopatica Polmonare (IPF), che è una sotto-categoria delle ILD.Source: ISTI Technical Reports 007/2020, 2020, 2020
DOI: 10.32079/isti-tr-2020/007
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Master thesis
Open Access
Analisi di immagini tomografiche ad alta risoluzione attraverso reti neurali convoluzionali per lo studio delle interstiziopatie polmonari
Buongiorno R.
The term Interstitial Lung Disease (ILD) refers to a large group of lung disorders, most of which cause scars of the interstitium, usually referred to as pulmonary fibrosis. Fibrosis reduces the ability of the air sacs to capture and carry oxygen into the bloodstream, leading to a progressive loss of the ability to breathe. Although ILDs are rare if taken individually, together they represent the most frequent cause of non-obstructive chronic lung disease. Nowadays, there are more than 200 different types of ILDs with varying causes, prognosis and therapies. Thus, identifying the correct type of ILD is necessary to make an accurate diagnosis. The Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive fibrosing interstitial pneumonia, which is classified among the ILDs with the poorest prognosis. The high variability and unpredictability of IPF course have traditionally made its clinical management hard. The recent introduction of antifibrotic drugs has opened novel therapeutic options for mild to moderate IPF. In this respect, treatment decisions highly rely on the assessment and quantification of IPF impact on the interstitium and its progression over time. High-Resolution Computed Tomography (HRCT) has demonstrated to have a key role in this frame, as it represents a non-invasive diagnostic modality to evaluate and quantify the extent of lung interstitium affected by IPF. In fact, IPF shows a typical radiological pattern, called Usual Interstitial Pneumonia (UIP) pattern, whose presence is usually assessed by radiologists to diagnose IPF. The HRCT features that characterize the UIP pattern are the presence and positioning of specific lung parenchymal anomalies, known as honeycombing , ground-glass opacification and fine reticulation. These anomalies appear in the HRCT scans with specific textural characteristics that are detected via a visual inspection of the imaging data. Assessing the diffusion of these anomalies is instrumental to understand the impact of IPF and to monitor its evolution over time. Quantitative and reliable approaches are in high demand in this respect, as the visual examination by radiologists suffers, by its nature, of poor reproducibility. To overcome this issue, much research is being conducted to develop new techniques for automatic detection of lung diseases that may support radiologists during the diagnostic pathway, particularly in HRCT image analysis. Indeed, HRCT images evaluation by a Machine Learning (ML)- based algorithm might provide low-cost, reliable, real time automatic identification of UIP pattern with human-level accuracy in order to objectively quantify the percentage of lung volume affected by the disease in a reproducible way. The purpose of this study was to develop a tool for UIP pattern recognition in HRCT images of patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. UIP-net takes as input a lung HRCT image with 492x492 pixels and outputs the corresponding binary map for the discrimination of disease and normal tissue. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans from the same scanner, was used. The network performance yielded 83.7% BF-score and 84.6% sensitivity but in order to refine the binary masks produced by UIP-net, a post-processing operation was carried out. With post-processing, vessels, air-ways and tissue wrongly classified as belonging to the lungs were removed from the outputted masks. After post-processing, the results increased to 96.7% BF-score and 85.9% sensitivity. Thus, the network performance, in terms of BF-score and sensitivity, demonstrated that CNNs have the potential to reliably detect disease in order to evaluate its progression and become a supportive tool for radiologists. Future works include adding more data to the training set in order to add multiple layers to the network to distinguish and quantify the different HRCT features of UIP pattern, improving the reproducibility and reliability of the CNN and using it for the detection of HRCT manifestations of Covid-19.
Buongiorno R.
The term Interstitial Lung Disease (ILD) refers to a large group of lung disorders, most of which cause scars of the interstitium, usually referred to as pulmonary fibrosis. Fibrosis reduces the ability of the air sacs to capture and carry oxygen into the bloodstream, leading to a progressive loss of the ability to breathe. Although ILDs are rare if taken individually, together they represent the most frequent cause of non-obstructive chronic lung disease. Nowadays, there are more than 200 different types of ILDs with varying causes, prognosis and therapies. Thus, identifying the correct type of ILD is necessary to make an accurate diagnosis. The Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive fibrosing interstitial pneumonia, which is classified among the ILDs with the poorest prognosis. The high variability and unpredictability of IPF course have traditionally made its clinical management hard. The recent introduction of antifibrotic drugs has opened novel therapeutic options for mild to moderate IPF. In this respect, treatment decisions highly rely on the assessment and quantification of IPF impact on the interstitium and its progression over time. High-Resolution Computed Tomography (HRCT) has demonstrated to have a key role in this frame, as it represents a non-invasive diagnostic modality to evaluate and quantify the extent of lung interstitium affected by IPF. In fact, IPF shows a typical radiological pattern, called Usual Interstitial Pneumonia (UIP) pattern, whose presence is usually assessed by radiologists to diagnose IPF. The HRCT features that characterize the UIP pattern are the presence and positioning of specific lung parenchymal anomalies, known as honeycombing , ground-glass opacification and fine reticulation. These anomalies appear in the HRCT scans with specific textural characteristics that are detected via a visual inspection of the imaging data. Assessing the diffusion of these anomalies is instrumental to understand the impact of IPF and to monitor its evolution over time. Quantitative and reliable approaches are in high demand in this respect, as the visual examination by radiologists suffers, by its nature, of poor reproducibility. To overcome this issue, much research is being conducted to develop new techniques for automatic detection of lung diseases that may support radiologists during the diagnostic pathway, particularly in HRCT image analysis. Indeed, HRCT images evaluation by a Machine Learning (ML)- based algorithm might provide low-cost, reliable, real time automatic identification of UIP pattern with human-level accuracy in order to objectively quantify the percentage of lung volume affected by the disease in a reproducible way. The purpose of this study was to develop a tool for UIP pattern recognition in HRCT images of patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. UIP-net takes as input a lung HRCT image with 492x492 pixels and outputs the corresponding binary map for the discrimination of disease and normal tissue. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans from the same scanner, was used. The network performance yielded 83.7% BF-score and 84.6% sensitivity but in order to refine the binary masks produced by UIP-net, a post-processing operation was carried out. With post-processing, vessels, air-ways and tissue wrongly classified as belonging to the lungs were removed from the outputted masks. After post-processing, the results increased to 96.7% BF-score and 85.9% sensitivity. Thus, the network performance, in terms of BF-score and sensitivity, demonstrated that CNNs have the potential to reliably detect disease in order to evaluate its progression and become a supportive tool for radiologists. Future works include adding more data to the training set in order to add multiple layers to the network to distinguish and quantify the different HRCT features of UIP pattern, improving the reproducibility and reliability of the CNN and using it for the detection of HRCT manifestations of Covid-19.
See at:
etd.adm.unipi.it | ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
Can Magnetic Resonance Radiomics Analysis Discriminate Parotid Gland Tumors? A Pilot Study
Gabelloni M., Faggioni L., Attanasio S., Vani V., Goddi A., Colantonio S., Germanese D., Caudai C., Bruschini L., Scarano M., Seccia V., Neri E.
Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy.Source: Diagnostics (Basel) 10 (2020). doi:10.3390/diagnostics10110900
DOI: 10.3390/diagnostics10110900
Metrics:
Gabelloni M., Faggioni L., Attanasio S., Vani V., Goddi A., Colantonio S., Germanese D., Caudai C., Bruschini L., Scarano M., Seccia V., Neri E.
Our purpose is to evaluate the performance of magnetic resonance (MR) radiomics analysis for differentiating between malignant and benign parotid neoplasms and, among the latter, between pleomorphic adenomas and Warthin tumors. We retrospectively evaluated 75 T2-weighted images of parotid gland lesions, of which 61 were benign tumors (32 pleomorphic adenomas, 23 Warthin tumors and 6 oncocytomas) and 14 were malignant tumors. A receiver operating characteristics (ROC) curve analysis was performed to find the threshold values for the most discriminative features and determine their sensitivity, specificity and area under the ROC curve (AUROC). The most discriminative features were used to train a support vector machine classifier. The best classification performance was obtained by comparing a pleomorphic adenoma with a Warthin tumor (yielding sensitivity, specificity and a diagnostic accuracy as high as 0.8695, 0.9062 and 0.8909, respectively) and a pleomorphic adenoma with malignant tumors (sensitivity, specificity and a diagnostic accuracy of 0.6666, 0.8709 and 0.8043, respectively). Radiomics analysis of parotid tumors on conventional T2-weighted MR images allows the discrimination of pleomorphic adenomas from Warthin tumors and malignant tumors with a high sensitivity, specificity and diagnostic accuracy.Source: Diagnostics (Basel) 10 (2020). doi:10.3390/diagnostics10110900
DOI: 10.3390/diagnostics10110900
Metrics:
See at:
Diagnostics | Diagnostics | ISTI Repository | Diagnostics | Diagnostics | CNR ExploRA
2019
Journal article
Open Access
Radiomics to support precision medicine in oncology
Colantonio S, Barucci A., Germanese D.
Precision health, the future of patient care, is dependent on artificial intelligence. Of the information contained in a digital medical image, visual analysis can only extract about 10%. Radiomics aims to extract an enormous wealth of quantitative data from biomedical images, which could not be processed through simple visual analysis, but is capable of providing more information on the underlying pathophysiological phenomena and biological processes within human body. The subsequent mining of these quantitative data can offer very useful information on the aggressiveness of the disease under investigation, opening at the tailoring of the therapies based on a patient's needs and at the monitoring of the response to care. Therefore, by using specific mathematical algorithms and artificial intelligence techniques, radiomics provides very powerful support for precision medicine, especially in oncology.Source: ERCIM news (2019): 15–16.
Colantonio S, Barucci A., Germanese D.
Precision health, the future of patient care, is dependent on artificial intelligence. Of the information contained in a digital medical image, visual analysis can only extract about 10%. Radiomics aims to extract an enormous wealth of quantitative data from biomedical images, which could not be processed through simple visual analysis, but is capable of providing more information on the underlying pathophysiological phenomena and biological processes within human body. The subsequent mining of these quantitative data can offer very useful information on the aggressiveness of the disease under investigation, opening at the tailoring of the therapies based on a patient's needs and at the monitoring of the response to care. Therefore, by using specific mathematical algorithms and artificial intelligence techniques, radiomics provides very powerful support for precision medicine, especially in oncology.Source: ERCIM news (2019): 15–16.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2021
Conference article
Open Access
UIP-net: a decoder-encoder CNN for the detection and quantification of usual interstitial pneumoniae pattern in lung CT scan images
Buongiorno R., Germanese D., Romei C., Tavanti L., De Liperi A., Colantonio S.
A key step of the diagnosis of Idiopathic Pulmonary Fibrosis (IPF) is the examination of high-resolution computed tomography images (HRCT). IPF exhibits a typical radiological pattern, named Usual Interstitial Pneumoniae (UIP) pattern, which can be detected in non-invasive HRCT investigations, thus avoiding surgical lung biopsy. Unfortunately, the visual recognition and quantification of UIP pattern can be challenging even for experienced radiologists due to the poor inter and intra-reader agreement. This study aimed to develop a tool for the semantic segmentation and the quantification of UIP pattern in patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. The proposed CNN, based on an encoder-decoder architecture, takes as input a thoracic HRCT image and outputs a binary mask for the automatic discrimination between UIP pattern and healthy lung parenchyma. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans of different patients, was used. The network performance yielded 96.7% BF-score and 85.9% sensitivity. Once trained and tested, the UIP-net was used to obtain the segmentations of other 60 CT scans of different patients to estimate the volume of lungs affected by the UIP pattern. The measurements were compared with those obtained using the reference software for the automatic detection of UIP pattern, named Computer Aided Lungs Informatics for Pathology Evaluation and Rating (CALIPER), through the Bland-Altman plot. The network performance assessed in terms of both BF-score and sensitivity on the test-set and resulting from the comparison with CALIPER demonstrated that CNNs have the potential to reliably detect and quantify pulmonary disease in order to evaluate its progression and become a supportive tool for radiologists.Source: ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges, pp. 389–405, Milan, Italy - Virtual event, 10-15/01/2021
DOI: 10.1007/978-3-030-68763-2_30
Metrics:
Buongiorno R., Germanese D., Romei C., Tavanti L., De Liperi A., Colantonio S.
A key step of the diagnosis of Idiopathic Pulmonary Fibrosis (IPF) is the examination of high-resolution computed tomography images (HRCT). IPF exhibits a typical radiological pattern, named Usual Interstitial Pneumoniae (UIP) pattern, which can be detected in non-invasive HRCT investigations, thus avoiding surgical lung biopsy. Unfortunately, the visual recognition and quantification of UIP pattern can be challenging even for experienced radiologists due to the poor inter and intra-reader agreement. This study aimed to develop a tool for the semantic segmentation and the quantification of UIP pattern in patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. The proposed CNN, based on an encoder-decoder architecture, takes as input a thoracic HRCT image and outputs a binary mask for the automatic discrimination between UIP pattern and healthy lung parenchyma. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans of different patients, was used. The network performance yielded 96.7% BF-score and 85.9% sensitivity. Once trained and tested, the UIP-net was used to obtain the segmentations of other 60 CT scans of different patients to estimate the volume of lungs affected by the UIP pattern. The measurements were compared with those obtained using the reference software for the automatic detection of UIP pattern, named Computer Aided Lungs Informatics for Pathology Evaluation and Rating (CALIPER), through the Bland-Altman plot. The network performance assessed in terms of both BF-score and sensitivity on the test-set and resulting from the comparison with CALIPER demonstrated that CNNs have the potential to reliably detect and quantify pulmonary disease in order to evaluate its progression and become a supportive tool for radiologists.Source: ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges, pp. 389–405, Milan, Italy - Virtual event, 10-15/01/2021
DOI: 10.1007/978-3-030-68763-2_30
Metrics:
See at:
link.springer.com | ISTI Repository | link.springer.com | CNR ExploRA
2022
Contribution to book
Unknown
Artificial Intelligence for chest imaging against COVID-19: an insight into image segmentation methods
Buongiorno R., Germanese D., Colligiani L., Fanni S. C., Romei C., Colantonio S.
The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2, emerged in late 2019 and soon developed as a pandemic leading to a world health crisis. Chest imaging examination plays a vital role in the clinical management and prognostic evaluation of COVID-19 since the imaging pathological findings reflect the inflammatory process of the lungs. Particularly, thanks to its highest sensitivity and resolution, Computer Tomography chest imaging serves well in the distinction of the different parenchymal patterns and manifestations of COVID-19. It is worth noting that detecting and quantifying such manifestations is a key step in evaluating disease impact and tracking its progression or regression over time. Nevertheless, the visual inspection or, even worse, the manual delimitation of such manifestations may be greatly time-consuming and overwhelming for radiologists, especially when pressed by the urgent needs of patient care. Image segmentation tools, powered by Artificial Intelligence, may sensibly reduce radiologists' workload as they may automate or, at least, facilitate the delineation of the pathological lesions and the other regions of interest for disease assessment. This delineation lays the basis for further diagnostic and prognostic analyses based on quantitative information extracted from the segmented lesions. This chapter overviews the Artificial Intelligence methods for the segmentation of chest Computed Tomography images. The focus is in particular on Deep Learning approaches, as these have lately become the mainstream approach to image segmentation. A novel method, leveraging attention-based learning, is presented and evaluated. Finally, a discussion of the potential, limitations, and still open challenges of the field concludes the chapter.Source: Artificial Intelligence in Healthcare and COVID-19. Amsterdam: Elsevier, 2022
Buongiorno R., Germanese D., Colligiani L., Fanni S. C., Romei C., Colantonio S.
The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2, emerged in late 2019 and soon developed as a pandemic leading to a world health crisis. Chest imaging examination plays a vital role in the clinical management and prognostic evaluation of COVID-19 since the imaging pathological findings reflect the inflammatory process of the lungs. Particularly, thanks to its highest sensitivity and resolution, Computer Tomography chest imaging serves well in the distinction of the different parenchymal patterns and manifestations of COVID-19. It is worth noting that detecting and quantifying such manifestations is a key step in evaluating disease impact and tracking its progression or regression over time. Nevertheless, the visual inspection or, even worse, the manual delimitation of such manifestations may be greatly time-consuming and overwhelming for radiologists, especially when pressed by the urgent needs of patient care. Image segmentation tools, powered by Artificial Intelligence, may sensibly reduce radiologists' workload as they may automate or, at least, facilitate the delineation of the pathological lesions and the other regions of interest for disease assessment. This delineation lays the basis for further diagnostic and prognostic analyses based on quantitative information extracted from the segmented lesions. This chapter overviews the Artificial Intelligence methods for the segmentation of chest Computed Tomography images. The focus is in particular on Deep Learning approaches, as these have lately become the mainstream approach to image segmentation. A novel method, leveraging attention-based learning, is presented and evaluated. Finally, a discussion of the potential, limitations, and still open challenges of the field concludes the chapter.Source: Artificial Intelligence in Healthcare and COVID-19. Amsterdam: Elsevier, 2022
See at: CNR ExploRA
2023
Contribution to book
Open Access
Introduction to machine learning in medicine
Buongiorno R., Caudai C., Colantonio S., Germanese D.
This chapter aimed to describe, as simply as possible, what Machine Learning is and how it can be used fruitfully in the medical field.Source: Introduction to Artificial Intelligence, edited by Klontzas M.E., Fanni S.C., Neri E., pp. 39–68. Basel: Springer Nature Switzerland, 2023
DOI: 10.1007/978-3-031-25928-9_3
Metrics:
Buongiorno R., Caudai C., Colantonio S., Germanese D.
This chapter aimed to describe, as simply as possible, what Machine Learning is and how it can be used fruitfully in the medical field.Source: Introduction to Artificial Intelligence, edited by Klontzas M.E., Fanni S.C., Neri E., pp. 39–68. Basel: Springer Nature Switzerland, 2023
DOI: 10.1007/978-3-031-25928-9_3
Metrics:
See at:
ISTI Repository | doi.org | CNR ExploRA
2023
Journal article
Open Access
Enhancing COVID-19 CT image segmentation: a comparative study of attention and recurrence in UNet models
Buongiorno R., Del Corso G., Germanese D., Colligiani L., Python L., Romei C., Colantonio S.
Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81,93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application.Source: JOURNAL OF IMAGING (2023). doi:10.3390/jimaging9120283
DOI: 10.3390/jimaging9120283
Metrics:
Buongiorno R., Del Corso G., Germanese D., Colligiani L., Python L., Romei C., Colantonio S.
Imaging plays a key role in the clinical management of Coronavirus disease 2019 (COVID-19) as the imaging findings reflect the pathological process in the lungs. The visual analysis of High-Resolution Computed Tomography of the chest allows for the differentiation of parenchymal abnormalities of COVID-19, which are crucial to be detected and quantified in order to obtain an accurate disease stratification and prognosis. However, visual assessment and quantification represent a time-consuming task for radiologists. In this regard, tools for semi-automatic segmentation, such as those based on Convolutional Neural Networks, can facilitate the detection of pathological lesions by delineating their contour. In this work, we compared four state-of-the-art Convolutional Neural Networks based on the encoder-decoder paradigm for the binary segmentation of COVID-19 infections after training and testing them on 90 HRCT volumetric scans of patients diagnosed with COVID-19 collected from the database of the Pisa University Hospital. More precisely, we started from a basic model, the well-known UNet, then we added an attention mechanism to obtain an Attention-UNet, and finally we employed a recurrence paradigm to create a Recurrent-Residual UNet (R2-UNet). In the latter case, we also added attention gates to the decoding path of an R2-UNet, thus designing an R2-Attention UNet so as to make the feature representation and accumulation more effective. We compared them to gain understanding of both the cognitive mechanism that can lead a neural model to the best performance for this task and the good compromise between the amount of data, time, and computational resources required. We set up a five-fold cross-validation and assessed the strengths and limitations of these models by evaluating the performances in terms of Dice score, Precision, and Recall defined both on 2D images and on the entire 3D volume. From the results of the analysis, it can be concluded that Attention-UNet outperforms the other models by achieving the best performance of 81,93%, in terms of 2D Dice score, on the test set. Additionally, we conducted statistical analysis to assess the performance differences among the models. Our findings suggest that integrating the recurrence mechanism within the UNet architecture leads to a decline in the model's effectiveness for our particular application.Source: JOURNAL OF IMAGING (2023). doi:10.3390/jimaging9120283
DOI: 10.3390/jimaging9120283
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2015
Report
Unknown
SEMEOTICONS - Gas sensors for breath analysis
D'Acunto M., Germanese D., Magrini M., Paradisi P., Righi M., Pagliei E., Gimeno M.
In this deliverable 3.5.1, we report the first 15-months activity within the Work Packages 3 (Multispectral data analysis and sensors development) task 3.5. The task 3.5 provides the manufacturing of a device for breath analysis: the Wize Sniffer (WS), a new portable device for breath analysis limited to an effective number of substances.Source: Project report, SEMEOTICONS, Deliverable D3.5.1, 2015
Project(s): SEMEOTICONS
D'Acunto M., Germanese D., Magrini M., Paradisi P., Righi M., Pagliei E., Gimeno M.
In this deliverable 3.5.1, we report the first 15-months activity within the Work Packages 3 (Multispectral data analysis and sensors development) task 3.5. The task 3.5 provides the manufacturing of a device for breath analysis: the Wize Sniffer (WS), a new portable device for breath analysis limited to an effective number of substances.Source: Project report, SEMEOTICONS, Deliverable D3.5.1, 2015
Project(s): SEMEOTICONS
See at: CNR ExploRA
2015
Report
Unknown
SEMEOTICONS - Final specification of system requirements and functionalities
Colantonio S., Germanese D., Martinelli M., Righi M., Coppini G., Chiarugi F., Pediaditis M., Stromberg T., Bjorgan A., Henriquez P., Matuszewski B. J., Nicoletta A., Vitali I., Miliarakis A., Assimakopoulou C.
This deliverable summarizes the final set of system requirements and functional specifications of the Wize Mirror and its components. It merges together and add new updates to D2.1.1 "Initial specification of system requirements and functionalities" and D2.1.2 "Revised specification of system requirements and functionalities".Source: Project report, SEMEOTICONS, Deliverable D2.1.3, 2015
Project(s): SEMEOTICONS
Colantonio S., Germanese D., Martinelli M., Righi M., Coppini G., Chiarugi F., Pediaditis M., Stromberg T., Bjorgan A., Henriquez P., Matuszewski B. J., Nicoletta A., Vitali I., Miliarakis A., Assimakopoulou C.
This deliverable summarizes the final set of system requirements and functional specifications of the Wize Mirror and its components. It merges together and add new updates to D2.1.1 "Initial specification of system requirements and functionalities" and D2.1.2 "Revised specification of system requirements and functionalities".Source: Project report, SEMEOTICONS, Deliverable D2.1.3, 2015
Project(s): SEMEOTICONS
See at: CNR ExploRA
2017
Conference article
Open Access
A low cost technology-based device for breath analysis and self-monitoring
Germanese D., D'Acunto M., Magrini M., Righi M., Salvetti O.
Here, we describe the development of a portable device, based on low cost technology, able to collect and analyze in real time the composition of the breath. Despite its great potential, breath analysis is not widely used in clinical practice: high costs for standard analytical instrumentation (i.e., gas chromatograph- mass spectrometer), the need for specialized personnel able to read the results and the lack of standardized protocols to collect breath samples, set limits to its exploitation. The presented device, named Wize Sniffer, is based on commercial gas sensors and a widely employed open-source controller; in addition, it is very easy to use also for non-specialized personnel. The Wize Sniffer is composed of three modules: signal measurement, signal conditioning and signal processing. The idea was born in the framework of the European SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self- monitoring (SEMEOTICONS) Project, in order to monitor indi- vidual's lifestyle by detecting in the breath those molecules related to the noxious habits for cardio-metabolic risk. Nonetheless, the modular configuration of the Wize Sniffer makes it usable also for other applications by changing the type of the gas sensors according to the molecules to be detected.Source: SIGNAL 2017 The Second International Conference on Advances in Signal, Image and Video Processing, pp. 8–13, Barcellona, Spain, 21-25 May 2017
Project(s): SEMEOTICONS
Germanese D., D'Acunto M., Magrini M., Righi M., Salvetti O.
Here, we describe the development of a portable device, based on low cost technology, able to collect and analyze in real time the composition of the breath. Despite its great potential, breath analysis is not widely used in clinical practice: high costs for standard analytical instrumentation (i.e., gas chromatograph- mass spectrometer), the need for specialized personnel able to read the results and the lack of standardized protocols to collect breath samples, set limits to its exploitation. The presented device, named Wize Sniffer, is based on commercial gas sensors and a widely employed open-source controller; in addition, it is very easy to use also for non-specialized personnel. The Wize Sniffer is composed of three modules: signal measurement, signal conditioning and signal processing. The idea was born in the framework of the European SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self- monitoring (SEMEOTICONS) Project, in order to monitor indi- vidual's lifestyle by detecting in the breath those molecules related to the noxious habits for cardio-metabolic risk. Nonetheless, the modular configuration of the Wize Sniffer makes it usable also for other applications by changing the type of the gas sensors according to the molecules to be detected.Source: SIGNAL 2017 The Second International Conference on Advances in Signal, Image and Video Processing, pp. 8–13, Barcellona, Spain, 21-25 May 2017
Project(s): SEMEOTICONS
See at:
ISTI Repository | CNR ExploRA
2017
Journal article
Open Access
Cardio-metabolic diseases prevention by self-monitoring the breath
Germanese D, D'Acunto M., Magrini M., Righi M., Salvetti O.
As new as very promising technique, breath analysis allows for monitoring the biochemical processes that occur in human body in a non-invasive way. Nevertheless, the high costs for standard analytical instrumentation (i.e., gas chromatograph, mass spectrometer), the need for specialized personnel able to read the results and the lack of protocols to collect breath samples, set limit to the exploitation of breath analysis in clinical practice. Here, we describe the development of a device, named Wize Sniffer, which is portable and entirely based on low cost technology: it uses an array of commercial, semiconductor gas sensors and a widely employed open source controller, an Arduino Mega2560 with Ethernet module. In addition, it is very easy-to-use also for non-specialized personnel and able to analyze in real time the composition of the breath. The Wize Sniffer is composed of three modules: signal measurement module, signal conditioning module and signal processing module. The idea was born in the framework of European SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self-monitoring (SEMEOTICONS) Project, in order to monitor individual's lifestyle by detecting in the breath those molecules related to the noxious habits for cardio-metabolic risk (alcohol intake, smoking, wrong diet). Nonetheless, the modular configuration of the device allows for changing the sensors according to the molecules to be detected, thus fully exploiting the potential of breath analysis.Source: IFSA-news (Online) 215 (2017): 19–26.
Project(s): SEMEOTICONS
Germanese D, D'Acunto M., Magrini M., Righi M., Salvetti O.
As new as very promising technique, breath analysis allows for monitoring the biochemical processes that occur in human body in a non-invasive way. Nevertheless, the high costs for standard analytical instrumentation (i.e., gas chromatograph, mass spectrometer), the need for specialized personnel able to read the results and the lack of protocols to collect breath samples, set limit to the exploitation of breath analysis in clinical practice. Here, we describe the development of a device, named Wize Sniffer, which is portable and entirely based on low cost technology: it uses an array of commercial, semiconductor gas sensors and a widely employed open source controller, an Arduino Mega2560 with Ethernet module. In addition, it is very easy-to-use also for non-specialized personnel and able to analyze in real time the composition of the breath. The Wize Sniffer is composed of three modules: signal measurement module, signal conditioning module and signal processing module. The idea was born in the framework of European SEMEiotic Oriented Technology for Individual's CardiOmetabolic risk self-assessmeNt and Self-monitoring (SEMEOTICONS) Project, in order to monitor individual's lifestyle by detecting in the breath those molecules related to the noxious habits for cardio-metabolic risk (alcohol intake, smoking, wrong diet). Nonetheless, the modular configuration of the device allows for changing the sensors according to the molecules to be detected, thus fully exploiting the potential of breath analysis.Source: IFSA-news (Online) 215 (2017): 19–26.
Project(s): SEMEOTICONS
See at:
ISTI Repository | www.sensorsportal.com | CNR ExploRA
2017
Conference article
Open Access
Self-monitoring the breath for the prevention of cardio-metabolic risk
Germanese D., D'Acunto M., Magrini M., Righi M., Salvetti O.
Breath analysis techniques offer a potential revolution in health care diagnostics because of their un-obtrusiveness and their inherent safety. However, while standard instrumentation such as mass spectrometers use laboratory settings to provide a correlation between exhaled substances and physical conditions, to fully realize the potential of breath analysis as a self-monitoring tool, its application must take place also in the clinics and at home and not only in a laboratory. This basic requirement has stimulated the necessity to develop cheap, portable, real time, easy-to-use devices for reliable breath tests and analysis. In this paper, we present the design of a portable breath analyzer, able to sense a set of breath volatile organic compounds (VOCs), to perform a processing of the data collected and to generate an output easily interpreted both by physicians and patients.Source: Eighth International Conference on Sensor Device Technologies and Applications, pp. 96–101, Rome, Italy, 10-14/09/2017
Project(s): SEMEOTICONS
Germanese D., D'Acunto M., Magrini M., Righi M., Salvetti O.
Breath analysis techniques offer a potential revolution in health care diagnostics because of their un-obtrusiveness and their inherent safety. However, while standard instrumentation such as mass spectrometers use laboratory settings to provide a correlation between exhaled substances and physical conditions, to fully realize the potential of breath analysis as a self-monitoring tool, its application must take place also in the clinics and at home and not only in a laboratory. This basic requirement has stimulated the necessity to develop cheap, portable, real time, easy-to-use devices for reliable breath tests and analysis. In this paper, we present the design of a portable breath analyzer, able to sense a set of breath volatile organic compounds (VOCs), to perform a processing of the data collected and to generate an output easily interpreted both by physicians and patients.Source: Eighth International Conference on Sensor Device Technologies and Applications, pp. 96–101, Rome, Italy, 10-14/09/2017
Project(s): SEMEOTICONS
See at:
ISTI Repository | CNR ExploRA
2017
Journal article
Open Access
The wize sniffer knows what you did: prevent cardio-metabolic risk by analyzing your breath
Germanese D., D'Acunto M., Righi M., Magrini M., Salvetti O.
Its un-obtrusiveness and its inherent safety make breath analysis a very promising technique in health-care diagnostics. On one hand, it enables the monitoring of biochemical processes: the volatile organic compounds (VOCs) from the metabolic processes are generated within the body, travel via the blood, participate to the alveolar exchanges and appear in exhaled breath; on the other hand, breath is easily and non-invasively accessible. Nevertheless, despite its great potential, breath analysis is not widely used in clinical practice: the high costs for standard analytical instrumentation (i.e., gas chromatograph-mass spectrometer), the need for specialized personnel able to read the results and the lack of standardized protocols to collect breath samples, set limits to its exploitation. Here, we describe the Wize Sniffer (WS), a portable device based on low cost technology, able to collect and analyze in real time the composition of the breath. In particular, by means of the WS, the user can evaluate his/her own cardio-metabolic risk score by self-monitoring the composition of the breath. Indeed, the presented device is able to detect, in real time, all those VOCs related to the noxious habits for cardio-metabolic risk. Nonetheless, the modular configuration of the WS, makes it usable also for other applications by changing the type of the gas sensors according to the molecules to be detected.Source: International journal on advances in life sciences 9 (2017): 198–207.
Project(s): SEMEOTICONS
Germanese D., D'Acunto M., Righi M., Magrini M., Salvetti O.
Its un-obtrusiveness and its inherent safety make breath analysis a very promising technique in health-care diagnostics. On one hand, it enables the monitoring of biochemical processes: the volatile organic compounds (VOCs) from the metabolic processes are generated within the body, travel via the blood, participate to the alveolar exchanges and appear in exhaled breath; on the other hand, breath is easily and non-invasively accessible. Nevertheless, despite its great potential, breath analysis is not widely used in clinical practice: the high costs for standard analytical instrumentation (i.e., gas chromatograph-mass spectrometer), the need for specialized personnel able to read the results and the lack of standardized protocols to collect breath samples, set limits to its exploitation. Here, we describe the Wize Sniffer (WS), a portable device based on low cost technology, able to collect and analyze in real time the composition of the breath. In particular, by means of the WS, the user can evaluate his/her own cardio-metabolic risk score by self-monitoring the composition of the breath. Indeed, the presented device is able to detect, in real time, all those VOCs related to the noxious habits for cardio-metabolic risk. Nonetheless, the modular configuration of the WS, makes it usable also for other applications by changing the type of the gas sensors according to the molecules to be detected.Source: International journal on advances in life sciences 9 (2017): 198–207.
Project(s): SEMEOTICONS
See at:
ISTI Repository | www.iariajournals.org | CNR ExploRA