Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

• Published in: BMC medical imaging Vol. 24; no. 1; p. 50
• Main Authors: Yin, Minyue, Xu, Chao, Zhu, Jinzhou, Xue, Yuhan, Zhou, Yijia, He, Yu, Lin, Jiaxi, Liu, Lu, Gao, Jingwen, Liu, Xiaolin, Shen, Dan, Fu, Cuiping
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.02.2024; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Asymptomatic; Automated machine learning; Automation; Bibliometrics; Chest; Computed tomography; Coronaviruses; COVID-19; CT imaging; Diagnostic imaging; Disease transmission; Epidemics; Generalized linear models; Hospitals; Learning algorithms; Machine learning; Medical imaging; Medical imaging equipment; Medical research; Medicine, Experimental; Neural networks; Pandemics; Pneumonia; Prediction model; Prediction models; Rankings; Sensitivity; Sensitivity analysis; Software; Statistical analysis; Technology application; COVID-19; Automated machine learning; Asymptomatic; Prediction model
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-024-01211-w

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.