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

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Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
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Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
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Department of Computer Science Engineering, PSIT, Kanpur 209305, India
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Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy
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Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy
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Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
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Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy
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The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
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Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
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Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
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Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece
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Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
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Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
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Men’s Health Center, Miriam Hospital, Providence, RI 02912, USA
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Rheumatology Unit, National Kapodistrian University of Athens, 17674 Athens, Greece
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Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
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Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
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Department of Immunology, SGPIMS, Lucknow 226014, India
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Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
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Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
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Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
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Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada
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AtheroPoint LLC., Roseville, CA 95661, USA
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Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Engomi 2408, Cyprus
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Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22902, USA
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Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
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Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
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Neurology Department, Fortis Hospital, Bengaluru 560076, India
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Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary
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Invasive Cardiology Division, University of Szeged, 1122 Budapest, Hungary
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Department of ECE, Idaho State University, Pocatello, ID 83209, USA
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Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
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Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
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Author to whom correspondence should be addressed.
Academic Editor: Andor W.J.M. Glaudemans
Diagnostics 2022, 12(6), 1482; https://doi.org/10.3390/diagnostics12061482
Received: 24 May 2022 / Revised: 7 June 2022 / Accepted: 13 June 2022 / Published: 16 June 2022
(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans. View Full-Text
Keywords: COVID-19 lesion; lung CT; Hounsfield units; glass ground opacities; hybrid deep learning; explainable AI; segmentation; classification; GRAD-CAM; Grad-CAM++; Score-CAM; FasterScore-CAM COVID-19 lesion; lung CT; Hounsfield units; glass ground opacities; hybrid deep learning; explainable AI; segmentation; classification; GRAD-CAM; Grad-CAM++; Score-CAM; FasterScore-CAM
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MDPI and ACS Style

Suri, J.S.; Agarwal, S.; Chabert, G.L.; Carriero, A.; Paschè, A.; Danna, P.S.C.; Saba, L.; Mehmedović, A.; Faa, G.; Singh, I.M.; Turk, M.; Chadha, P.S.; Johri, A.M.; Khanna, N.N.; Mavrogeni, S.; Laird, J.R.; Pareek, G.; Miner, M.; Sobel, D.W.; Balestrieri, A.; Sfikakis, P.P.; Tsoulfas, G.; Protogerou, A.D.; Misra, D.P.; Agarwal, V.; Kitas, G.D.; Teji, J.S.; Al-Maini, M.; Dhanjil, S.K.; Nicolaides, A.; Sharma, A.; Rathore, V.; Fatemi, M.; Alizad, A.; Krishnan, P.R.; Nagy, F.; Ruzsa, Z.; Fouda, M.M.; Naidu, S.; Viskovic, K.; Kalra, M.K. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics 2022, 12, 1482. https://doi.org/10.3390/diagnostics12061482

AMA Style

Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics. 2022; 12(6):1482. https://doi.org/10.3390/diagnostics12061482

Chicago/Turabian Style

Suri, Jasjit S., Sushant Agarwal, Gian Luca Chabert, Alessandro Carriero, Alessio Paschè, Pietro S. C. Danna, Luca Saba, Armin Mehmedović, Gavino Faa, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Athanasios D. Protogerou, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Jagjit S. Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, Ferenc Nagy, Zoltan Ruzsa, Mostafa M. Fouda, Subbaram Naidu, Klaudija Viskovic, and Mannudeep K. Kalra. 2022. "COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans" Diagnostics 12, no. 6: 1482. https://doi.org/10.3390/diagnostics12061482

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