Diagnostic test accuracy of deep learning detection of COVID-19: a systematic review and meta-analysis

Komolafe, Temitope Emmanuel; Cao, Yuzhu; Nguchu, Benedictor Alexander; Monkam, Patrice; Olaniyi, Ebenezer Obaloluwa; Sun, Haotian; Zheng, Jian; Yang, Xiaodong

RATIONALE AND OBJECTIVE: To perform a meta-analysis to compare the diagnostic test accuracy (DTA) of deep learning (DL) in detecting coronavirus disease 2019 (COVID-19), and to investigate how network architecture and type of datasets affect DL performance. MATERIALS AND METHODS: We searched PubMed, Web of Science and Inspec from January 1, 2020, to December 3, 2020, for retrospective and prospective studies on deep learning detection with at least reported sensitivity and specificity. Pooled DTA was obtained using random-effect models. Sub-group analysis between studies was also carried out for data source and network architectures. RESULTS: The pooled sensitivity and specificity were 91% (95% confidence interval [CI]: 88%, 93%; [Formula: see text] = 69%) and 92% (95% CI: 88%, 94%; [Formula: see text] = 88%), respectively for 19 studies. The pooled AUC and diagnostic odds ratio (DOR) were 0.95 (95% CI: 0.88, 0.92) and 112.5 (95% CI: 57.7, 219.3; [Formula: see text] = 90%) respectively. The overall accuracy, recall, F1-score, [Formula: see text] and [Formula: see text] are 89.5%, 89.5%, 89.7%, 23.13 and 0.13. Sub-group analysis shows that the sensitivity and DOR significantly vary with the type of network architectures and sources of data with low heterogeneity are ([Formula: see text] =0%) and ([Formula: see text] =18%) for ResNet architecture and single-source datasets, respectively. CONCLUSION: The diagnosis of COVID-19 via deep learning has achieved incredible performance, and the source of datasets, as well as network architectures, strongly affect DL performance.