Accurate and Rapid Diagnosis of COVID-19 Pneumonia with Batch Effect Removal of Chest CT-Scans and Interpretable Artificial Intelligence

• URL: http://arxiv.org/abs/2011.11736v2
• Date: Fri, 8 Jan 2021 07:08:00 GMT
• Title: Accurate and Rapid Diagnosis of COVID-19 Pneumonia with Batch Effect Removal of Chest CT-Scans and Interpretable Artificial Intelligence
• Authors: Rassa Ghavami Modegh (1,2), Mehrab Hamidi (1,2), Saeed Masoudian (1), Amir Mohseni (1), Hamzeh Lotfalinezhad (1), Mohammad Ali Kazemi (3), Behnaz Moradi (3), Mahyar Ghafoori (4), Omid Motamedi (4), Omid Pournik (4), Kiara Rezaei-Kalantari (5), Amirreza Manteghinezhad (6,7), Shaghayegh Haghjooy Javanmard (6,7), Fateme Abdoli Nezhad (8), Ahmad Enhesari (8), Mohammad Saeed Kheyrkhah (9), Razieh Eghtesadi (10), Javid Azadbakht (11), Akbar Aliasgharzadeh (10), Mohammad Reza Sharif (12), Ali Khaleghi (13), Abbas Foroutan (14), Hossein Ghanaati (3), Hamed Dashti (1), Hamid R. Rabiee (1,2) ((1) AI-Med Group, AI Innovation Center, Sharif University of Technology, Tehran, Iran, (2) DML Lab, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran, (3) Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran, (4) Preventive Medicine and Public Health Research Center, Psychosocial Health Research Institute, Community and Family Medicine Department, School of Medicine, Iran University of Medical Sciences, Tehran, Iran, (5) Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran, (6) Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan, Iran, (7) University of Medical Science, Isfahan, Iran, (8) Kerman University of Medical Sciences, Kerman, Iran, (9) Research Institute of Animal Embryo Technology, Shahrekord University, Shahrekord, Iran, (10) Kashan University of Medical Sciences, Kashan, Iran, (11) Department of Radiology, Kashan University of Medical Sciences, Kashan, Iran, (12) Department of Pediatrics, Kashan University of Medical Sciences, Kashan, Iran, (13) Department of Computer Engineering, Imam Khomeini International University, Qazvin, Iran, (14) Shaheed Beheshti University of Medical Sciences, Medical Academy of Science, Tehran, Iran)
• Abstract summary: We designed a new interpretable deep neural network to distinguish healthy people, patients with COVID-19, and patients with other pneumonia diseases from CT-scan images. The model reached sensitivities of 97.75% and 98.15%, and specificities of 87% and 81.03% in separating healthy people from the diseased and COVID-19 from other diseases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: COVID-19 is a virus with high transmission rate that demands rapid identification of the infected patients to reduce the spread of the disease. The current gold-standard test, Reverse-Transcription Polymerase Chain Reaction (RT-PCR), has a high rate of false negatives. Diagnosing from CT-scan images as a more accurate alternative has the challenge of distinguishing COVID-19 from other pneumonia diseases. Artificial intelligence can help radiologists and physicians to accelerate the process of diagnosis, increase its accuracy, and measure the severity of the disease. We designed a new interpretable deep neural network to distinguish healthy people, patients with COVID-19, and patients with other pneumonia diseases from axial lung CT-scan images. Our model also detects the infected areas and calculates the percentage of the infected lung volume. We first preprocessed the images to eliminate the batch effects of different devices, and then adopted a weakly supervised method to train the model without having any tags for the infected parts. We trained and evaluated the model on a large dataset of 3359 samples from 6 different medical centers. The model reached sensitivities of 97.75% and 98.15%, and specificities of 87% and 81.03% in separating healthy people from the diseased and COVID-19 from other diseases, respectively. It also demonstrated similar performance for 1435 samples from 6 different medical centers which proves its generalizability. The performance of the model on a large diverse dataset, its generalizability, and interpretability makes it suitable to be used as a reliable diagnostic system.

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