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2021 Report Restricted

Studio Pink - Le linee di sviluppo
Pieroni S., Franchini M., Denoth F., Colantonio S., Tampucci M., Fortunato L., Molinaro S.
Questo documento descrive le linee di sviluppo dello studio P.I.N.K., già previste come parte integrante del progetto fin dall'avvio delle attività, che si fondano sull'attivazione di studi ad hoc trasversali alle diverse tematiche trattate. Sono di fatto tre linee di ricerca parallele che partono dalla solida base di conoscenza creata all'interno di P.I.N.K. nei suoi primi tre anni di vita: linea 1 riguardante Imaging e Radiomica, linea 2 riguardante la dosimetria personalizzata, linea 3 riguardante la nutrizione e stile di vita.Source: Project report, Studio Pink, 2021

See at: CNR ExploRA Restricted


2021 Report Open Access OPEN

Technical report on the development and interpretation of convolutional neural networks for the classification of multiparametric MRI images on unbalanced datasets. Case study: prostate cancer
Pachetti E., Colantonio S.
This report summarized the activities carried out to define, train and validate Deep Learning models for the classification of medical imaging data. The issue of unbalanced datasets was faced by applying some data augmentation techniques, based on transformation of the original images. Such techniques were compared to verify their impact in a frame where object morphology is relevant. Multimodal deep learning models were defined to exploit the information contained in heterogeneous imaging data and cope with data distribution imbalance. To verify the inner functioning of the deep learning models, the LIME algorithm was applied, thus checking that the regions that contribute to the classification were the real meaningful ones. The case study used to was the categorization of prostate cancer aggressiveness based on Magnetic Resonance Imaging (MRI) data. The aggressiveness was determined, as a ground truth, via tissue biopsy and expressed with a score from 2 to 10 known as Gleason Score, which is obtained as the sum of two values, each one from 1 to 5, associated with the two most common patterns in the tumor tissue histological sample.Source: ISTI Technical Report, ISTI-2021-TR/005, pp.1–16, 2021

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2021 Conference article Open Access OPEN

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

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | link.springer.com Restricted


2021 Report Open Access OPEN

SI-Lab Annual Research Report 2020
Leone G. R., Righi M., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Technical Report, ISTI-2021-TR/009, pp.1–38, 2021
DOI: 10.32079/isti-tr-2021/009

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2021 Conference article Restricted

A deep Learning approach for hepatic steatosis estimation from ultrasound imaging
Colantonio S., Salvati A., Caudai C., Bonino F., De Rosa L., Pascali M. A., Germanese D., Brunetto M. R., Faita F.
This paper proposes a simple convolutional neural model as a novel method to predict the level of hepatic steatosis from ultrasound data. Hepatic steatosis is the major histologic feature of non-alcoholic fatty liver disease (NAFLD), which has become a major global health challenge. Recently a new definition for FLD, that take into account the risk factors and clinical characteristics of subjects, has been suggested; the proposed criteria for Metabolic Disfunction-Associated Fatty Liver Disease (MAFLD) are based on histological (biopsy), imaging or blood biomarker evidence of fat accumulation in the liver (hepatic steatosis), in subjects with overweight/obesity or presence of type 2 diabetes mellitus. In lean or normal weight, non-diabetic individuals with steatosis, MAFLD is diagnosed when at least two metabolic abnormalities are present. Ultrasound examinations are the most used technique to non-invasively identify liver steatosis in a screening settings. However, the diagnosis is operator dependent, as accurate image processing techniques have not entered yet in the diagnostic routine. In this paper, we discuss the adoption of simple convolutional neural models to estimate the degree of steatosis from echographic images in accordance with the state-of-the-art magnetic resonance spectroscopy measurements (expressed as percentage of the estimated liver fat). More than 22,000 ultrasound images were used to train three networks, and results show promising performances in our study (150 subjects).Source: ICCCI 2021 - 13th International Conference on Computational Collective Intelligence, pp. 703–714, Rhodes, Greece, 29/09/2021,1/10/ 2021
DOI: 10.1007/978-3-030-88113-9_57

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2020 Contribution to conference Open Access OPEN

Augmented reality and intelligent systems in Industry 4.0
Benassi A., Carboni A., Colantonio S., Coscetti S., Germanese D., Jalil B., Leone R., Magnavacca J., Magrini M., Martinelli M., Matarese F., Moroni D., Paradisi P., Pardini F., Pascali M., Pieri G., Reggiannini M., Righi M., Salvetti O., Tampucci M.
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESSource: ARTES, 12/11/2020
DOI: 10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

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

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Master thesis Open Access OPEN

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.

See at: etd.adm.unipi.it Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Journal article Open Access OPEN

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

See at: Diagnostics Open Access | Europe PubMed Central Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Diagnostics Open Access | Diagnostics Open Access | Diagnostics Open Access


2020 Report Open Access OPEN

CNR Foresight project - MAD4Future (Models, Algorithms, and Data for the Future) working group - background document
Colantonio S., Bacco F. M.
The Science and Technology Foresight Project seeks to define a medium to long-term vision - 5 to 30 years - in order to elaborate coherent research strategies relevant to socially critical problems in the field of environment, health, food, energy, security and transportation. Both, the holistic approach applied to the analysis of the topics, as well as the innovative format of the invitation-only workshops, enticed the participation of internationally acknowledged experts. This framework guaranteed all participants the necessary conditions to carry out an open interactive debate, consolidating a collective intelligence, which assisted in achieving a consensus on research priorities, knowledge gaps, and funding needs. This approach is designed to facilitate convergence towards common positions in order to address the social acceptability of future products and services and resultant market potential. A strong consensus among the workshops' participants during the past four years has been reached regarding the urgency of scientific and technological breakthroughs in a number of issues, strongly correlated to each other, which need to be tackled. The issues can be summarized as follows: (i) development of materials to perform different functions according to external environmental conditions and to respond to different requirements; (ii) interaction between AI, data, models, and knowledge in order to design learning machines that provide satisfactory explanations to humans for their decisions, continuously learn to respond to unknown conditions, and robustly handle adversarial examples; (iii) systems considered as a global entity with all kinds of dimensions and always analysed by parts with proper interfaces. The study of the interactions between the interfaces, the dynamics and the communication ways is of primary importance for understanding the functioning of the systems themselves, even if the nature of the interactions can be very varied and may require multi-scaling treatment. The aim of the present document is to identify foresight priorities within the topic "Models, Algorithms, and Data for the Future", which indicate a roadmap for the discussion in future workshops.Source: ISTI Working Papers, pp.1–9, 2020

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.foresight.cnr.it Open Access


2019 Journal article Open Access OPEN

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

See at: Sensors Open Access | Sensors Open Access | Sensors Open Access | Europe PubMed Central Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Sensors Open Access | Sensors Open Access | Sensors Open Access | Sensors Open Access


2019 Conference article Open Access OPEN

La radiomica come elemento fondante della medicina di precisione in ambito oncologico
Colantonio S., Carlini E., Caudai C., Germanese D., Manghi P., Pascali M. A., Barucci A., Farnesi D., Zoppetti N., Colcelli V., Pini R., Carpi R., Esposito M., Neri E., Romei C., Occhipinti M.
Questo documento introduce e inquadra le attività che un gruppo interdisciplinare di ricercatori e clinici sta portando avanti grazie a tecniche di analisi di immagini, machine learning e intelligenza artificiale, a supporto della medicina di precisione in ambito oncologico. Partendo dalla comprensione del fenomeno fisico e dalla caratterizzazione dei processi biologici che sottendono alla formazione delle immagini biomedicali, attraverso tecniche di analisi radiomica dei dati radiologici e di mining di dati complessi, terogenei e multisorgente, le soluzioni studiate mirano a supportare i clinici nel continuum dei processi diagnostici, prognostici e terapeutici in ambito oncologico.Source: Ital-IA: primo Convegno Nazionale CINI sull'Intelligenza Artificiale, Roma, Italy, 18-19 marzo 2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.ital-ia.it Open Access


2019 Conference article Restricted

Radiomics to predict prostate cancer aggressiveness: a preliminary study
Germanese D., Mercatelli L., Colantonio S., Miele V., Pascali M. A., Caudai C., Zoppetti N., Carpi R., Barucci A., Bertelli E., Agostini S.
Radiomics is encouraging a paradigm shift in oncological diagnostics towards the symbiosis of radiology and Artificial Intelligence (AI) techniques. The aim is to exploit very accurate, robust image processing algorithms and provide quantitative information about the phenotypic differences of cancer traits. By exploring the association between this quantitative information and patients' prognosis, AI algorithms are boosting the power of radiomics in the perspective of precision oncology. However, the choice of the most suitable AI method can determine the success of a radiomic application. The current state-of-the art methods in radiomics aim at extracting statistical features from biomedical images and, then, process them with Machine Learning (ML) techniques. Many works have been reported in the literature presenting various combinations of radiomic features and ML methods. In this preliminary study, we aim to analyse the performance of a radiomic approach to predict prostate cancer (PCa) aggressiveness from multiarametric Magnetic Resonance Imaging (mp-MRI). Clinical mp-MRI data were collected from patients with histology-confirmed PCa and labelled by a team of expert radiologists. Such data were used to extract and select two sets of radiomic features; hence, the classification performances of five classifiers were assessed. This analysis is meant as a preliminary step towards the overall goal of investigating the potential of radiomic-based analyses.Source: BIBE 2019: 19th annual IEEE International Conference on Bioinformatics and Bioengineering, pp. 972–976, Athens, Greece, 28-30 October 2019
DOI: 10.1109/bibe.2019.00181

See at: academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2019 Conference article Open Access OPEN

May radiomic data predict prostate cancer aggressiveness?
Germanese D., Colantonio S., Caudai C., Pascali M. A., Barucci A., Zoppetti N., Agostini S., Bertelli E., Mercatelli L., Miele V., Carpi R.
Radiomics can quantify tumor phenotypic characteristics non-invasively by defining a signature correlated with biological information. Thanks to algorithms derived from computer vision to extract features from images, and machine learning methods to mine data, Radiomics is the perfect case study of application of Artificial Intelligence in the context of precision medicine. In this study we investigated the association between radiomic features extracted from multi-parametric magnetic resonance imaging (mp-MRI)of prostate cancer (PCa) and the tumor histologic subtypes (using Gleason Score) using machine learning algorithms, in order to identify which of the mp-MRI derived radiomic features can distinguish high and low risk PCa.Source: CAIP 2019 - International Conference on Computer Analysis of Images and Patterns, pp. 65–75, Salerno, Italy, 6 September, 2019
DOI: 10.1007/978-3-030-29930-9_7

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2019 Conference article Open Access OPEN

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

See at: ISTI Repository Open Access | academic.microsoft.com Restricted | dblp.uni-trier.de Restricted | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted | xplorestaging.ieee.org Restricted


2019 Conference article Open Access OPEN

La manutenzione predittiva nel Progetto CompTo-NM: ottimizzare lo sviluppo di un dispositivo innovativo di imaging biomedicale ibrido
Carlini E., Colantonio S.
La manutenzione predittiva in ambiti industriali e? un'attivita? che ha recentemente subito un notevole sviluppo, grazie alla sempre crescente quantita? di dati disponibili e all'abilita? dei sistemi software di ultima generazione di saperli gestire. Questo documento presenta le attivita? di manutenzione predittiva che sono previste all'interno del progetto Regione Toscana CompTo-NM, concentrandosi sulle tecniche di rilevazione di anomalie basate su metodi di apprendimento automatico.Source: Ital-IA: primo Convegno Nazionale CINI sull'Intelligenza Artificiale - Workshop AI for Industrial Automation, Roma, Italy, 18-19 marzo 2019

See at: CNR ExploRA Open Access | www.ital-ia.it Open Access


2019 Contribution to journal Open Access OPEN

The Digital Health revolution
Colantonio S., Ayache N.
Source: ERCIM news 118 (2019): 4–5.

See at: ercim-news.ercim.eu Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Journal article Open Access OPEN

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.

See at: ercim-news.ercim.eu Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2018 Other Restricted

Studio PINK - Piattaforma di raccolta dati
Pieroni S., Tampucci M., Franchini M., Denoth F., Colantonio S., Molinaro S.
Il documento fornisce una guida operativa al personale dei centri diagnostici partecipanti allo Studio PINK e utenti della piattaforma di inserimento dati epidemiologici e clinici che vengono raccolti per lo studio osservazionale.

See at: CNR ExploRA Restricted


2018 Report Restricted

Studio Pink - Struttura del database per la raccolta dati
Pieroni S., Franchini M., Tampucci M., Martinelli M., Denoth F., Colantonio S., Molinaro S.
Il documento fornisce la specifica del database relazionale alla base della piattaforma web di supporto alla gestione, alla archiviazione digitale e alle elaborazioni statistiche dei dati dello studio PINK Prevention Imaging Network & Knowledge.Source: ISTI Technical reports, 2018

See at: CNR ExploRA Restricted