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2023
Journal article
Open Access
GPU accelerated digital twins of the human heart open new routes for cardiovascular research
Viola F., Del Corso G., De Paulis R., Verzicco R.
The recruitment of patients for rare or complex cardiovascular diseases is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative. In this paper we present an unprecedented cardiovascular computer model which, relying on the latest GPU-acceleration technologies, replicates the full multi-physics dynamics of the human heart within a few hours per heartbeat. This opens the way to extensive simulation campaigns to study the response of synthetic cohorts of patients to cardiovascular disorders, novel prosthetic devices or surgical procedures. As a proof-of-concept we show the results obtained for left bundle branch block disorder and the subsequent cardiac resynchronization obtained by pacemaker implantation. The in-silico results closely match those obtained in clinical practice, confirming the reliability of the method. This innovative approach makes possible a systematic use of digital twins in cardiovascular research, thus reducing the need of real patients with their economical and ethical implications. This study is a major step towards in-silico clinical trials in the era of digital medicine.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-34098-8
DOI: 10.1038/s41598-023-34098-8
Metrics:
Viola F., Del Corso G., De Paulis R., Verzicco R.
The recruitment of patients for rare or complex cardiovascular diseases is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative. In this paper we present an unprecedented cardiovascular computer model which, relying on the latest GPU-acceleration technologies, replicates the full multi-physics dynamics of the human heart within a few hours per heartbeat. This opens the way to extensive simulation campaigns to study the response of synthetic cohorts of patients to cardiovascular disorders, novel prosthetic devices or surgical procedures. As a proof-of-concept we show the results obtained for left bundle branch block disorder and the subsequent cardiac resynchronization obtained by pacemaker implantation. The in-silico results closely match those obtained in clinical practice, confirming the reliability of the method. This innovative approach makes possible a systematic use of digital twins in cardiovascular research, thus reducing the need of real patients with their economical and ethical implications. This study is a major step towards in-silico clinical trials in the era of digital medicine.Source: Scientific reports (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-023-34098-8
DOI: 10.1038/s41598-023-34098-8
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ISTI Repository 
| www.nature.com | CNR ExploRA
2023
Journal article
Embargo
Six-food elimination diet is less effective during pollen season in adults with eosinophilic esophagitis sensitized to pollens
Visaggi P., Savarino E., Del Corso G., Hunter H., Baiano Svizzero F., Till S. J., Jason D., Wong T., De Bortoli N., Zeki S.
The role of inhaled and swallowed aeroallergens in treatment outcomes of adult patients with eosinophilic esophagitis (EoE) is unclear. We hypothesized that the pollen season contributes to the failure of the 6-food elimination diet (SFED) in EoE. We compared outcomes of patients with EoE who underwent SFED during vs outside of the pollen season. Consecutive adult patients with EoE who underwent SFED and skin prick test (SPT) for birch and grass pollen were included. Individual pollen sensitization and pollen count data were analyzed to define whether each patient had been assessed during or outside of the pollen season after SFED. All patients had active EoE (>=15 eosinophils/high-power field) before SFED and adhered to the diet under the supervision of a dietitian. Fifty-eight patients were included, 62.0% had positive SPT for birch and/or grass, whereas 37.9% had negative SPT. Overall, SFED response was 56.9% (95% confidence interval, 44.1%-68.8%). When stratifying response according to whether the assessment had been performed during or outside of the pollen season, patients sensitized to pollens showed significantly lower response to SFED during compared with outside of the pollen season (21.4% vs 77.3%; P = 0.003). In addition, during the pollen season, patients with pollen sensitization had significantly lower response to SFED compared with those without sensitization (21.4% vs 77.8%; P = 0.01). Pollens may have a role in sustaining esophageal eosinophilia in sensitized adults with EoE despite avoidance of trigger foods. The SPT for pollens may identify patients less likely to respond to the diet during the pollen season.Source: The American journal of gastroenterology (2023). doi:10.14309/ajg.0000000000002357
DOI: 10.14309/ajg.0000000000002357
Metrics:
Visaggi P., Savarino E., Del Corso G., Hunter H., Baiano Svizzero F., Till S. J., Jason D., Wong T., De Bortoli N., Zeki S.
The role of inhaled and swallowed aeroallergens in treatment outcomes of adult patients with eosinophilic esophagitis (EoE) is unclear. We hypothesized that the pollen season contributes to the failure of the 6-food elimination diet (SFED) in EoE. We compared outcomes of patients with EoE who underwent SFED during vs outside of the pollen season. Consecutive adult patients with EoE who underwent SFED and skin prick test (SPT) for birch and grass pollen were included. Individual pollen sensitization and pollen count data were analyzed to define whether each patient had been assessed during or outside of the pollen season after SFED. All patients had active EoE (>=15 eosinophils/high-power field) before SFED and adhered to the diet under the supervision of a dietitian. Fifty-eight patients were included, 62.0% had positive SPT for birch and/or grass, whereas 37.9% had negative SPT. Overall, SFED response was 56.9% (95% confidence interval, 44.1%-68.8%). When stratifying response according to whether the assessment had been performed during or outside of the pollen season, patients sensitized to pollens showed significantly lower response to SFED during compared with outside of the pollen season (21.4% vs 77.3%; P = 0.003). In addition, during the pollen season, patients with pollen sensitization had significantly lower response to SFED compared with those without sensitization (21.4% vs 77.8%; P = 0.01). Pollens may have a role in sustaining esophageal eosinophilia in sensitized adults with EoE despite avoidance of trigger foods. The SPT for pollens may identify patients less likely to respond to the diet during the pollen season.Source: The American journal of gastroenterology (2023). doi:10.14309/ajg.0000000000002357
DOI: 10.14309/ajg.0000000000002357
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journals.lww.com 
| CNR ExploRA
2023
Journal article
Restricted
Comparison of drugs for active eosinophilic oesophagitis: systematic review and network meta-analysis
Visaggi P., Barberio B., Del Corso G., De Bortoli N., Black K. J., Ford A. C. Savarino E.
Background: There is currently no recommendation regarding preferred drugs for active eosinophilic oesophagitis (EoE) because their relative efficacy is unclear. We conducted an up-to-date network meta-analysis to compare proton pump inhibitors, off-label and EoE-specific topical steroids, and biologics in EoE. Methods: We searched MEDLINE, Embase, Embase Classic and the Cochrane Central Register of Controlled Trials from inception to June 2023. We included randomised controlled trials (RCTs) comparing efficacy of all drugs versus each other, or placebo, in adults and adolescents with active EoE. Results were reported as pooled relative risks with 95% CIs to summarise effect of each comparison tested, with drugs ranked according to P score Results: Seventeen RCTs were eligible for systematic review. Of these, 15 studies containing 1813 subjects with EoE reported extractable data for the network meta-analysis. For histological remission defined as <=6 eosinophils/high-power field (HPF), lirentelimab 1 mg/kg monthly ranked first. For histological remission defined as <=15 eosinophils/HPF, budesonide orally disintegrating tablet (BOT) 1 mg two times per day ranked first. For failure to achieve symptom improvement, BOT 1 mg two times per day and budesonide oral suspension (BOS) 2 mg two times per day were significantly more efficacious than placebo. For failure to achieve endoscopic improvement based on the EoE Endoscopic Reference Score, BOT 1 mg two times per day and BOS 1 mg two times per day or 2 mg two times per day were significantly more efficacious than placebo. Conclusions: Although this network meta-analysis supports the efficacy of most available drugs over placebo for EoE treatment, significant heterogeneity in eligibility criteria and outcome measures among available trials hampers the establishment of a solid therapeutic hierarchy.Source: Gut 72 (2023): 2019–2030. doi:10.1136/gutjnl-2023-329873
DOI: 10.1136/gutjnl-2023-329873
Metrics:
Visaggi P., Barberio B., Del Corso G., De Bortoli N., Black K. J., Ford A. C. Savarino E.
Background: There is currently no recommendation regarding preferred drugs for active eosinophilic oesophagitis (EoE) because their relative efficacy is unclear. We conducted an up-to-date network meta-analysis to compare proton pump inhibitors, off-label and EoE-specific topical steroids, and biologics in EoE. Methods: We searched MEDLINE, Embase, Embase Classic and the Cochrane Central Register of Controlled Trials from inception to June 2023. We included randomised controlled trials (RCTs) comparing efficacy of all drugs versus each other, or placebo, in adults and adolescents with active EoE. Results were reported as pooled relative risks with 95% CIs to summarise effect of each comparison tested, with drugs ranked according to P score Results: Seventeen RCTs were eligible for systematic review. Of these, 15 studies containing 1813 subjects with EoE reported extractable data for the network meta-analysis. For histological remission defined as <=6 eosinophils/high-power field (HPF), lirentelimab 1 mg/kg monthly ranked first. For histological remission defined as <=15 eosinophils/HPF, budesonide orally disintegrating tablet (BOT) 1 mg two times per day ranked first. For failure to achieve symptom improvement, BOT 1 mg two times per day and budesonide oral suspension (BOS) 2 mg two times per day were significantly more efficacious than placebo. For failure to achieve endoscopic improvement based on the EoE Endoscopic Reference Score, BOT 1 mg two times per day and BOS 1 mg two times per day or 2 mg two times per day were significantly more efficacious than placebo. Conclusions: Although this network meta-analysis supports the efficacy of most available drugs over placebo for EoE treatment, significant heterogeneity in eligibility criteria and outcome measures among available trials hampers the establishment of a solid therapeutic hierarchy.Source: Gut 72 (2023): 2019–2030. doi:10.1136/gutjnl-2023-329873
DOI: 10.1136/gutjnl-2023-329873
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Gut | gut.bmj.com | Gut | CNR ExploRA
2023
Conference article
Open Access
Exploring the potentials and challenges of AI in supporting clinical diagnostics and remote assistance for the health and well-being of individuals
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: Ital-IA 2023 - Italia Intelligenza Artificiale. Thematic Workshops of the 3rd CINI National Lab AIIS Conference on Artificial Intelligence - 2023, Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
Berti A., Buongiorno R., Carloni G., Caudai C., Del Corso G., Germanese D., Pachetti E., Pascali M. A., Colantonio S.
Innovative technologies powered by Artificial Intelligence have the big potential to support new models of care delivery, disease prevention and quality of life promotion. The ultimate goal is a paradigm shift towards more personalized, accessible, effective, and sustainable care and health systems. Nevertheless, despite the advances in the field over the last years, the adoption and deployment of AI technologies remains limited in clinical practice and real-world settings. This paper summarizes the activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out for exploring both the potential of AI in health and well-being as well as the challenges to their uptake in real-world settingsSource: Ital-IA 2023 - Italia Intelligenza Artificiale. Thematic Workshops of the 3rd CINI National Lab AIIS Conference on Artificial Intelligence - 2023, Pisa, Italy, 29-30/05/2023
Project(s): ProCAncer-I
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
AI trustworthiness in prostate cancer imaging: a look at algorithmic and system transparency
Colantonio S., Berti A., Buongiorno R., Del Corso G., Pachetti E., Pascali M. A., Kalantzopoulos C., Kalokyri V., Kondylakis H., Tachos N., Fotiadis D., Giannini V., Mazzetti S., Regge D., Papanikolaou N., Marias K., Tsiknakis M.
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Source: IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023
Project(s): ProCAncer-I
Colantonio S., Berti A., Buongiorno R., Del Corso G., Pachetti E., Pascali M. A., Kalantzopoulos C., Kalokyri V., Kondylakis H., Tachos N., Fotiadis D., Giannini V., Mazzetti S., Regge D., Papanikolaou N., Marias K., Tsiknakis M.
A responsible approach to artificial intelligence and machine learning technologies, grounded in sound scientific foundations, technical robustness, rigorous testing and validation, risk-based continuous monitoring and alignment with human values is imperative to guarantee their favourable impact and prevent any adverse effects they may have on individuals and communities. An essential aspect of responsible development is transparency, which constitutes a fundamental principle of the European approach towards artificial intelligence. Transparency can be achieved at different levels, such as data origin and use, system development, operation and usage. In this paper, we present the techniques implemented and delivered in the EU H2020 ProCAncer-I project to meet the transparency requirements at the different levels required.Source: IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, Malta, 7-9/12/2023
Project(s): ProCAncer-I
See at:
ISTI Repository | 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
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ISTI Repository | www.mdpi.com | CNR ExploRA