Development and validation of an automated radiomic CT signature for detecting COVID-19

Abstract

Background The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits.Objectives To develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. MethodsIn this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance of the model. The datasets were collected from 2 different hospital sites of the CHU Liège, Belgium. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity.Results 1381 patients were included in this study. The average age was 64.4±15.8 and 63.8±14.4 years with a gender balance of 56% and 52% male in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value of the algorithm was found to be larger than 97%.All rights reserved. No reuse allowed without permission.was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

“…Diagnostic models using CT scans and traditional machine learning methods. Eight papers employed traditional machine learning methods for COVID-19 diagnosis using hand-engineered features 40,[57][58][59][60][61][62] or convolutional neural network (CNN)-extracted features 46 . Four papers 46,59,60,62 incorporated clinical features with those obtained from the CT images.…”

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

“…The potential benefits of radiomics had been highlighted in improving diagnostic, prognostic, and predictive accuracy for cancers such as lung cancer, rectal cancer, etc. as well as other non-neoplastic diseases [13][14][15][16]. To date, there are limited data about the value of chest CT-based radiomics in rapidly and accurately detecting COVID-19 pneumonia.…”

Applications of artificial intelligence in battling against covid-19: A literature review

“…Other examples of approaches that use machine learning techniques for processing CT scan and X-Ray images can be found in [353] , [354] , [355] , [356] , [357] , [358] , [359] , [360] , [361] , [362] , [363] , [364] , [365] , [366] , [367] , [368] , [369] , [370] , [371] .…”