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1. Lung vessel volume evaluated with CALIPER software is an independent predictor of mortality in COVID-19 patients: a multicentric retrospective analysis

Author

Romei, Chiara, Falaschi, Zeno, Danna, Pietro S. C., Airoldi, Chiara, Tonerini, Michele, Rocchi, Erika, Fanni, Salvatore C., D’Amelio, Claudio, Barbieri, Greta, Tiseo, Giusy, Arioli, Roberto, Paschè, Alessio, Karwoski, Ronald A., De Liperi, Annalisa, Bartholmai, Brian J., and Carriero, Alessandro

Published
2022
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2. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

Author

Agarwal, Mohit, Agarwal, Sushant, Saba, Luca, Chabert, Gian Luca, Gupta, Suneet, Carriero, Alessandro, Pasche, Alessio, Danna, Pietro, Mehmedovic, Armin, Faa, Gavino, Shrivastava, Saurabh, Jain, Kanishka, Jain, Harsh, Jujaray, Tanay, Singh, Inder M., Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Khanna, Narendra N., Mavrogeni, Sophie, Laird, John R., Sobel, David W., Miner, Martin, Balestrieri, Antonella, Sfikakis, Petros P., Tsoulfas, George, Misra, Durga Prasanna, Agarwal, Vikas, Kitas, George D., Teji, Jagjit S., Al-Maini, Mustafa, Dhanjil, Surinder K., Nicolaides, Andrew, Sharma, Aditya, Rathore, Vijay, Fatemi, Mostafa, Alizad, Azra, Krishnan, Pudukode R., Yadav, Rajanikant R., Nagy, Frence, Kincses, Zsigmond Tamás, Ruzsa, Zoltan, Naidu, Subbaram, Viskovic, Klaudija, Kalra, Manudeep K., and Suri, Jasjit S.

Published
2022
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3. Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation

Author

Suri, Jasjit S., Agarwal, Sushant, Saba, Luca, Chabert, Gian Luca, Carriero, Alessandro, Paschè, Alessio, Danna, Pietro, Mehmedović, Armin, Faa, Gavino, Jujaray, Tanay, Singh, Inder M., Khanna, Narendra N., Laird, John R., Sfikakis, Petros P., Agarwal, Vikas, Teji, Jagjit S., R Yadav, Rajanikant, Nagy, Ferenc, Kincses, Zsigmond Tamás, Ruzsa, Zoltan, Viskovic, Klaudija, and Kalra, Mannudeep K.

Published
2022
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5. Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

Author

Bellan, Mattia, Patti, Giuseppe, Hayden, Eyal, Azzolina, Danila, Pirisi, Mario, Acquaviva, Antonio, Aimaretti, Gianluca, Aluffi Valletti, Paolo, Angilletta, Roberto, Arioli, Roberto, Avanzi, Gian Carlo, Avino, Gianluca, Balbo, Piero Emilio, Baldon, Giulia, Baorda, Francesca, Barbero, Emanuela, Baricich, Alessio, Barini, Michela, Barone-Adesi, Francesco, Battistini, Sofia, Beltrame, Michela, Bertoli, Matteo, Bertolin, Stephanie, Bertolotti, Marinella, Betti, Marta, Bobbio, Flavio, Boffano, Paolo, Boglione, Lucio, Borrè, Silvio, Brucoli, Matteo, Calzaducca, Elisa, Cammarata, Edoardo, Cantaluppi, Vincenzo, Cantello, Roberto, Capponi, Andrea, Carriero, Alessandro, Casciaro, Francesco Giuseppe, Castello, Luigi Mario, Ceruti, Federico, Chichino, Guido, Chirico, Emilio, Cisari, Carlo, Cittone, Micol Giulia, Colombo, Crizia, Comi, Cristoforo, Croce, Eleonora, Daffara, Tommaso, Danna, Pietro, Della Corte, Francesco, De Vecchi, Simona, Dianzani, Umberto, Di Benedetto, Davide, Esposto, Elia, Faggiano, Fabrizio, Falaschi, Zeno, Ferrante, Daniela, Ferrero, Alice, Gagliardi, Ileana, Gaidano, Gianluca, Galbiati, Alessandra, Gallo, Silvia, Garavelli, Pietro Luigi, Gardino, Clara Ada, Garzaro, Massimiliano, Gastaldello, Maria Luisa, Gavelli, Francesco, Gennari, Alessandra, Giacomini, Greta Maria, Giacone, Irene, Giai Via, Valentina, Giolitti, Francesca, Gironi, Laura Cristina, Gramaglia, Carla, Grisafi, Leonardo, Inserra, Ilaria, Invernizzi, Marco, Krengli, Marco, Labella, Emanuela, Landi, Irene Cecilia, Landi, Raffaella, Leone, Ilaria, Lio, Veronica, Lorenzini, Luca, Maconi, Antonio, Malerba, Mario, Manfredi, Giulia Francesca, Martelli, Maria, Marzari, Letizia, Marzullo, Paolo, Mennuni, Marco, Montabone, Claudia, Morosini, Umberto, Mussa, Marco, Nerici, Ilaria, Nuzzo, Alessandro, Olivieri, Carlo, Padelli, Samuel Alberto, Panella, Massimiliano, Parisini, Andrea, Paschè, Alessio, Pau, Alberto, Pedrinelli, Anita Rebecca, Percivale, Ilaria, Re, Roberta, Rigamonti, Cristina, Rizzi, Eleonora, Rognoni, Andrea, Roveta, Annalisa, Salamina, Luigia, Santagostino, Matteo, Saraceno, Massimo, Savoia, Paola, Sciarra, Marco, Schimmenti, Andrea, Scotti, Lorenza, Spinoni, Enrico, Smirne, Carlo, Tarantino, Vanessa, Tillio, Paolo Amedeo, Vaschetto, Rosanna, Vassia, Veronica, Zagaria, Domenico, Zavattaro, Elisa, Zeppegno, Patrizia, Zottarelli, Francesca, and Sainaghi, Pier Paolo

Published
2020
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6. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework

Author

Dubey, Arun Kumar, Chabert, Gian Luca, Carriero, Alessandro, Pasche, Alessio, Danna, Pietro S. C., Agarwal, Sushant, Mohanty, Lopamudra, Nillmani, Lopamudra, Sharma, Neeraj, Yadav, Sarita, Jain, Achin, Kumar, Ashish, Kalra, Mannudeep K., Sobel, David W., Laird, John R., Singh, Inder M., Singh, Narpinder, Tsoulfas, George, Fouda, Mostafa M., Alizad, Azra, Kitas, George D., Khanna, Narendra N., Viskovic, Klaudija, Kukuljan, Melita, Al-Maini, Mustafa, El-Baz, Ayman, Saba, Luca, and Suri, Jasjit S.

Subjects
ResNet–UNet, transfer learning, ensemble deep learning, BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti. Radiologija, control, BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences. Radiology, COVID, unseen
Abstract

Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Published
2023

7. May an incidental finding on chest CT be a predictor of access in intensive care unit? Role of hepatic steatosis in patients affected by SARS-CoV-2

Author

Danna, Pietro S.C., primary, Buoni, Giada Francesca, additional, Bor, Simone, additional, Coda, Carolina, additional, Abruzzese, Flavia, additional, Bertoli, Matteo, additional, Giaivia, Valentina, additional, Airoldi, Chiara, additional, Castello, Luigi Mario, additional, Saba, Luca, additional, and Carriero, Alessandro, additional

Published
2022
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8. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Chabert, Gian Luca, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Saba, Luca, additional, Mehmedović, Armin, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Fouda, Mostafa M., additional, Naidu, Subbaram, additional, Viskovic, Klaudija, additional, and Kalra, Mannudeep K., additional

Published
2022
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9. COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Chabert, Gian Luca, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Saba, Luca, additional, Mehmedović, Armin, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios D., additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Fouda, Mostafa M., additional, Naidu, Subbaram, additional, Viskovic, Klaudija, additional, and Kalra, Manudeep K., additional

Published
2022
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11. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

Author

Suri, Jasjit S., primary, Agarwal, Sushant, additional, Carriero, Alessandro, additional, Paschè, Alessio, additional, Danna, Pietro S. C., additional, Columbu, Marta, additional, Saba, Luca, additional, Viskovic, Klaudija, additional, Mehmedović, Armin, additional, Agarwal, Samriddhi, additional, Gupta, Lakshya, additional, Faa, Gavino, additional, Singh, Inder M., additional, Turk, Monika, additional, Chadha, Paramjit S., additional, Johri, Amer M., additional, Khanna, Narendra N., additional, Mavrogeni, Sophie, additional, Laird, John R., additional, Pareek, Gyan, additional, Miner, Martin, additional, Sobel, David W., additional, Balestrieri, Antonella, additional, Sfikakis, Petros P., additional, Tsoulfas, George, additional, Protogerou, Athanasios, additional, Misra, Durga Prasanna, additional, Agarwal, Vikas, additional, Kitas, George D., additional, Teji, Jagjit S., additional, Al-Maini, Mustafa, additional, Dhanjil, Surinder K., additional, Nicolaides, Andrew, additional, Sharma, Aditya, additional, Rathore, Vijay, additional, Fatemi, Mostafa, additional, Alizad, Azra, additional, Krishnan, Pudukode R., additional, Nagy, Ferenc, additional, Ruzsa, Zoltan, additional, Gupta, Archna, additional, Naidu, Subbaram, additional, Paraskevas, Kosmas I., additional, and Kalra, Mannudeep K., additional

Published
2021
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12. CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

Author

Schiaffino, Simone, primary, Albano, Domenico, additional, Cozzi, Andrea, additional, Messina, Carmelo, additional, Arioli, Roberto, additional, Bnà, Claudio, additional, Bruno, Antonio, additional, Carbonaro, Luca A., additional, Carriero, Alessandro, additional, Carriero, Serena, additional, Danna, Pietro S. C., additional, D’Ascoli, Elisa, additional, De Berardinis, Claudia, additional, Della Pepa, Gianmarco, additional, Falaschi, Zeno, additional, Gitto, Salvatore, additional, Malavazos, Alexis E., additional, Mauri, Giovanni, additional, Monfardini, Lorenzo, additional, Paschè, Alessio, additional, Rizzati, Roberto, additional, Secchi, Francesco, additional, Vanzulli, Angelo, additional, Tombini, Valeria, additional, Vicentin, Ilaria, additional, Zagaria, Domenico, additional, Sardanelli, Francesco, additional, and Sconfienza, Luca M., additional

Published
2021
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13. Ct Findings of Covid-19 Pneumonia in Icu-Patients

Author

Cau, Riccardo, primary, Falaschi, Zeno, additional, Paschè, Alessio, additional, Danna, Pietro, additional, Arioli, Roberto, additional, Arru, Chiara D., additional, Zagaria, Domenico, additional, Tricca, Stefano, additional, Suri, Jasjit S., additional, Kalra, Mannudeep K., additional, Carriero, Alessandro, additional, and Saba, Luca, additional

Published
2021
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14. Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Author

Schiaffino, Simone, primary, Codari, Marina, additional, Cozzi, Andrea, additional, Albano, Domenico, additional, Alì, Marco, additional, Arioli, Roberto, additional, Avola, Emanuele, additional, Bnà, Claudio, additional, Cariati, Maurizio, additional, Carriero, Serena, additional, Cressoni, Massimo, additional, Danna, Pietro S. C., additional, Della Pepa, Gianmarco, additional, Di Leo, Giovanni, additional, Dolci, Francesco, additional, Falaschi, Zeno, additional, Flor, Nicola, additional, Foà, Riccardo A., additional, Gitto, Salvatore, additional, Leati, Giovanni, additional, Magni, Veronica, additional, Malavazos, Alexis E., additional, Mauri, Giovanni, additional, Messina, Carmelo, additional, Monfardini, Lorenzo, additional, Paschè, Alessio, additional, Pesapane, Filippo, additional, Sconfienza, Luca M., additional, Secchi, Francesco, additional, Segalini, Edoardo, additional, Spinazzola, Angelo, additional, Tombini, Valeria, additional, Tresoldi, Silvia, additional, Vanzulli, Angelo, additional, Vicentin, Ilaria, additional, Zagaria, Domenico, additional, Fleischmann, Dominik, additional, and Sardanelli, Francesco, additional

Published
2021
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15. Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

Author

Bellan, Mattia, primary, Azzolina, Danila, additional, Hayden, Eyal, additional, Gaidano, Gianluca, additional, Pirisi, Mario, additional, Acquaviva, Antonio, additional, Aimaretti, Gianluca, additional, Aluffi Valletti, Paolo, additional, Angilletta, Roberto, additional, Arioli, Roberto, additional, Avanzi, Gian Carlo, additional, Avino, Gianluca, additional, Balbo, Piero Emilio, additional, Baldon, Giulia, additional, Baorda, Francesca, additional, Barbero, Emanuela, additional, Baricich, Alessio, additional, Barini, Michela, additional, Barone-Adesi, Francesco, additional, Battistini, Sofia, additional, Beltrame, Michela, additional, Bertoli, Matteo, additional, Bertolin, Stephanie, additional, Bertolotti, Marinella, additional, Betti, Marta, additional, Bobbio, Flavio, additional, Boffano, Paolo, additional, Boglione, Lucio, additional, Borrè, Silvio, additional, Brucoli, Matteo, additional, Calzaducca, Elisa, additional, Cammarata, Edoardo, additional, Cantaluppi, Vincenzo, additional, Cantello, Roberto, additional, Capponi, Andrea, additional, Carriero, Alessandro, additional, Casciaro, Giuseppe Francesco, additional, Castello, Luigi Mario, additional, Ceruti, Federico, additional, Chichino, Guido, additional, Chirico, Emilio, additional, Cisari, Carlo, additional, Cittone, Micol Giulia, additional, Colombo, Crizia, additional, Comi, Cristoforo, additional, Croce, Eleonora, additional, Daffara, Tommaso, additional, Danna, Pietro, additional, Della Corte, Francesco, additional, De Vecchi, Simona, additional, Dianzani, Umberto, additional, Di Benedetto, Davide, additional, Esposto, Elia, additional, Faggiano, Fabrizio, additional, Falaschi, Zeno, additional, Ferrante, Daniela, additional, Ferrero, Alice, additional, Gagliardi, Ileana, additional, Galbiati, Alessandra, additional, Gallo, Silvia, additional, Garavelli, Pietro Luigi, additional, Gardino, Clara Ada, additional, Garzaro, Massimiliano, additional, Gastaldello, Maria Luisa, additional, Gavelli, Francesco, additional, Gennari, Alessandra, additional, Giacomini, Greta Maria, additional, Giacone, Irene, additional, Giai Via, Valentina, additional, Giolitti, Francesca, additional, Gironi, Laura Cristina, additional, Gramaglia, Carla, additional, Grisafi, Leonardo, additional, Inserra, Ilaria, additional, Invernizzi, Marco, additional, Krengli, Marco, additional, Labella, Emanuela, additional, Landi, Irene Cecilia, additional, Landi, Raffaella, additional, Leone, Ilaria, additional, Lio, Veronica, additional, Lorenzini, Luca, additional, Maconi, Antonio, additional, Malerba, Mario, additional, Manfredi, Giulia Francesca, additional, Martelli, Maria, additional, Marzari, Letizia, additional, Marzullo, Paolo, additional, Mennuni, Marco, additional, Montabone, Claudia, additional, Morosini, Umberto, additional, Mussa, Marco, additional, Nerici, Ilaria, additional, Nuzzo, Alessandro, additional, Olivieri, Carlo, additional, Padelli, Samuel Alberto, additional, Panella, Massimiliano, additional, Parisini, Andrea, additional, Paschè, Alessio, additional, Patrucco, Filippo, additional, Patti, Giuseppe, additional, Pau, Alberto, additional, Pedrinelli, Anita Rebecca, additional, Percivale, Ilaria, additional, Ragazzoni, Luca, additional, Re, Roberta, additional, Rigamonti, Cristina, additional, Rizzi, Eleonora, additional, Rognoni, Andrea, additional, Roveta, Annalisa, additional, Salamina, Luigia, additional, Santagostino, Matteo, additional, Saraceno, Massimo, additional, Savoia, Paola, additional, Sciarra, Marco, additional, Schimmenti, Andrea, additional, Scotti, Lorenza, additional, Spinoni, Enrico, additional, Smirne, Carlo, additional, Tarantino, Vanessa, additional, Tillio, Paolo Amedeo, additional, Tonello, Stelvio, additional, Vaschetto, Rosanna, additional, Vassia, Veronica, additional, Zagaria, Domenico, additional, Zavattaro, Elisa, additional, Zeppegno, Patrizia, additional, Zottarelli, Francesca, additional, and Sainaghi, Pier Paolo, additional

Published
2021
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16. COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

Author

Suri, Jasjit S. Agarwal, Sushant Carriero, Alessandro and Pasche, Alessio Danna, Pietro S. C. Columbu, Marta Saba, Luca Viskovic, Klaudija Mehmedovic, Armin Agarwal, Samriddhi and Gupta, Lakshya Faa, Gavino Singh, Inder M. Turk, Monika and Chadha, Paramjit S. Johri, Amer M. Khanna, Narendra N. and Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin and Sobel, David W. Balestrieri, Antonella Sfikakis, Petros P. and Tsoulfas, George Protogerou, Athanasios Misra, Durga Prasanna Agarwal, Vikas Kitas, George D. Teji, Jagjit S. and Al-Maini, Mustafa Dhanjil, Surinder K. Nicolaides, Andrew and Sharma, Aditya Rathore, Vijay Fatemi, Mostafa Alizad, Azra and Krishnan, Pudukode R. Nagy, Ferenc Ruzsa, Zoltan Gupta, Archna Naidu, Subbaram Paraskevas, Kosmas I. Kalra, Mannudeep K.

Abstract

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint(TM) Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland-Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of

Published
2021

17. COVLIAS 1.0 Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

Author

Suri, Jasjit S., Agarwal, Sushant, Chabert, Gian Luca, Carriero, Alessandro, Paschè, Alessio, Danna, Pietro S. C., Saba, Luca, Mehmedović, Armin, Faa, Gavino, Singh, Inder M., Turk, Monika, Chadha, Paramjit S., Johri, Amer M., Khanna, Narendra N., Mavrogeni, Sophie, Laird, John R., Pareek, Gyan, Miner, Martin, Sobel, David W., and Balestrieri, Antonella

Subjects
ARTIFICIAL intelligence, COVID-19, COMPUTED tomography, LUNGS, DEEP learning
Abstract

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test. [ABSTRACT FROM AUTHOR]

Published
2022
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18. Contribution of Atrial Fibrillation to In-Hospital Mortality in Patients With COVID-19

Author

Spinoni, Enrico Guido, primary, Mennuni, Marco, additional, Rognoni, Andrea, additional, Grisafi, Leonardo, additional, Colombo, Crizia, additional, Lio, Veronica, additional, Renda, Giulia, additional, Foglietta, Melissa, additional, Petrilli, Ivan, additional, D’Ardes, Damiano, additional, Sainaghi, Pier Paolo, additional, Aimaretti, Gianluca, additional, Bellan, Mattia, additional, Castello, Luigi, additional, Avanzi, Gian Carlo, additional, Corte, Francesco Della, additional, Krengli, Marco, additional, Pirisi, Mario, additional, Malerba, Mario, additional, Capponi, Andrea, additional, Gallina, Sabina, additional, Pierdomenico, Sante Donato, additional, Cipollone, Francesco, additional, Patti, Giuseppe, additional, Albano, Emanuele, additional, Dianzani, Umberto, additional, Gaidano, Gianluca, additional, Gennari, Alessandra, additional, Gramaglia, Carla, additional, Solli, Martina, additional, Giubertoni, Ailia, additional, Veia, Alessia, additional, Cisari, Carlo, additional, Amedeo Tillio, Paolo, additional, Valletti, Paolo Aluffi, additional, Adesi, Francesco Barone, additional, Barini, Michela, additional, Ferrante, Daniela, additional, De Vecchi, Simona, additional, Santagostino, Matteo, additional, Acquaviva, Antonio, additional, Calzaducca, Elisa, additional, Casciaro, Francesco Giuseppe, additional, Ceruti, Federico, additional, Cittone, Micol Giulia, additional, Di Benedetto, Davide, additional, Gagliardi, Ileana, additional, Giacomini, Greta Maria, additional, Landi, Irene Cecilia, additional, Landi, Raffaella, additional, Manfredi, Giulia Francesca, additional, Pedrinelli, Anita Rebecca, additional, Rigamonti, Cristina, additional, Rizzi, Eleonora, additional, Smirne, Carlo, additional, Vassia, Veronica, additional, Arioli, Roberto, additional, Danna, Pietro, additional, Falaschi, Zeno, additional, Paschè, Alessio, additional, Percivale, Ilaria, additional, Zagaria, Domenico, additional, Beltrame, Michela, additional, Bertoli, Matteo, additional, Galbiati, Alessandra, additional, Gardino, Clara Ada, additional, Gastaldello, Maria Luisa, additional, Via, Valentina Giai, additional, Giolitti, Francesca, additional, Inserra, Ilaria, additional, Labella, Emanuela, additional, Nerici, Ilaria, additional, Gironi, Laura Cristina, additional, Cammarata, Edoardo, additional, Esposto, Elia, additional, Tarantino, Vanessa, additional, Zavattaro, Elisa, additional, Zottarelli, Francesca, additional, Daffara, Tommaso, additional, Ferrero, Alice, additional, Leone, Ilaria, additional, Nuzzo, Alessandro, additional, Baldon, Giulia, additional, Battistini, Sofia, additional, Chirico, Emilio, additional, Lorenzini, Luca, additional, Martelli, Maria, additional, Barbero, Emanuela, additional, Boffano, Paolo, additional, Brucoli, Matteo, additional, Garzaro, Massimiliano, additional, Pau, Alberto, additional, Bertolin, Stephanie, additional, Marzari, Letizia, additional, Avino, Gianluca, additional, Saraceno, Massimo, additional, Morosini, Umberto, additional, Baricich, Alessio, additional, Invernizzi, Marco, additional, Gallo, Silvia, additional, Montabone, Claudia, additional, Padelli, Samuel Alberto, additional, Boglione, Lucio, additional, Patrucco, Filippo, additional, Salamina, Luigia, additional, Baorda, Francesca, additional, Croce, Eleonora, additional, and Giacone, Irene, additional

Published
2021
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19. Chest CT accuracy in diagnosing COVID-19 during the peak of the Italian epidemic: A retrospective correlation with RT-PCR testing and analysis of discordant cases

Author

Falaschi, Zeno, primary, Danna, Pietro S.C., additional, Arioli, Roberto, additional, Pasché, Alessio, additional, Zagaria, Domenico, additional, Percivale, Ilaria, additional, Tricca, Stefano, additional, Barini, Michela, additional, Aquilini, Ferruccio, additional, Andreoni, Stefano, additional, and Carriero, Alessandro, additional

Published
2020
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21. Impact of cancer in the management of delivery: 10 years of variations.

Author

Masturzo, Bianca, Parpinel, Giulia, Macchi, Chiara, De Ruvo, Daniele, Paracchini, Sara, Baima Poma, Cinzia, Danna, Pietro, Pagliardini, Greta, and Zola, Paolo

Subjects
PREMATURE labor, CESAREAN section, BIRTH weight, LOW birth weight, PATIENTS' attitudes
Abstract

Importance: The active-during-pregnancy-cancer (ADPC) is a condition that complicates the 0.1% of pregnancies. Abortion, preterm delivery and cesarean section (CS) are common attitudes for these patients, because of scarcity of evidence-based studies. Not-active-during-pregnancy-cancer (NADPC) is an increasing medical problem. The fertility of young girls survived to neoplasia is significantly lower compared to general population and there are increased rates of low birth weight and preterm birth.Objective: To analyze the impact that the pregnancy-related neoplastic disease has on management of deliveries in the decade 2006-2015.Material and methods: In this observational study, we collected obstetric and oncological data about 205 patients bearing a history of cancer related to pregnancy between January 2006 and September 2016 from Sant'Anna Hospital database archive in Turin. The entire population was divided in 59 patients with ADPC and 146 patients with NADPC because it was cured before starting the gestation. Three ADPC and three NADPC patients who completed their pregnancy in the year 2016 were excluded from the 10 years 2006-2015 trends realization. All in situ and invasive cancers were considered.Results: In ADPC patients, we registered 3.4% miscarriage and 15.3% iatrogenic abortion. The type of delivery was vaginal (22%) and CS (59.3%). Induction of labor was 14.6%, elective CS was 68.8%: the indication for these procedures was 78.6% oncological. The average gestational age was 35.5 weeks. In NADPC patients, we registered 9.6% miscarriage and 8.2% iatrogenic abortion. The type of delivery was vaginal (43.2%) and CS (39%). Induction of labor was 11.7%, elective CS was 36.7%: the indication for these procedures was 77.5% obstetrical. The average gestational age was 38.3 weeks.Conclusions: Ten-year trends in ADPC and NADPC patients showed an increase of induced deliveries and a decrease in elective CS. We observed not significant reduction of gestational age and birth weight. A contemporary decrease of oncological indications for CS in the two populations was reported. [ABSTRACT FROM AUTHOR]

Published
2020
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22. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

Author

Dubey AK, Chabert GL, Carriero A, Pasche A, Danna PSC, Agarwal S, Mohanty L, Nillmani, Sharma N, Yadav S, Jain A, Kumar A, Kalra MK, Sobel DW, Laird JR, Singh IM, Singh N, Tsoulfas G, Fouda MM, Alizad A, Kitas GD, Khanna NN, Viskovic K, Kukuljan M, Al-Maini M, El-Baz A, Saba L, and Suri JS

Abstract

Background and Motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks., Methodology: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability., Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability., Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Published
2023
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23. Computed tomography findings of COVID-19 pneumonia in Intensive Care Unit-patients.

Author

Cau R, Falaschi Z, Paschè A, Danna P, Arioli R, Arru CD, Zagaria D, Tricca S, Suri JS, Karla MK, Carriero A, and Saba L

Abstract

Background: In December 2019, a cluster of unknown etiology pneumonia cases occurred in Wuhan, China leading to identification of the responsible pathogen as SARS-coV-2. Since then, the coronavirus disease 2019 (COVID-19) has spread to the entire world. Computed Tomography (CT) is frequently used to assess severity and complications of COVID-19 pneumonia. The purpose of this study is to compare the CT patterns and clinical characteristics in intensive care unit (ICU) and non-ICU patients with COVID-19 pneumonia., Design and Methods: This retrospective study included 218 consecutive patients (136 males; 82 females; mean age 63±15 years) with laboratory-confirmed SARS-coV-2. Patients were categorized in two different groups: (a) ICU patients and (b) non-ICU inpatients. We assessed the type and extent of pulmonary opacities on chest CT exams and recorded the information on comorbidities and laboratory values for all patients., Results: Of the 218 patients, 23 (20 males: 3 females; mean age 60 years) required ICU admission, 195 (118 males: 77 females, mean age 64 years) were admitted to a clinical ward. Compared with non-ICU patients, ICU patients were predominantly males (60% versus 83% p=0.03), had more comorbidities, a positive CRP (p=0.04) and higher LDH values (p=0.008). ICU patients' chest CT demonstrated higher incidence of consolidation (p=0.03), mixed lesions (p=0.01), bilateral opacities (p<0.01) and overall greater lung involvement by consolidation (p=0.02) and GGO (p=0.001)., Conclusions: CT imaging features of ICU patients affected by COVID-19 are significantly different compared with non-ICU patients. Identification of CT features could assist in a stratification of the disease severity and supportive treatment.

Published
2021
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