Deep learning and machine learning in CT-based COPD diagnosis: Systematic review and meta-analysis

With advancements in medical technology and science, chronic obstructive pulmonary disease (COPD), one of the world's three major chronic diseases, has seen numerous remarkable outcomes when combined with artificial intelligence, particularly in disease diagnosis. However, the diagnostic performance of these AI models still lacks comprehensive evidence. Therefore, this study quantitatively analyzed the diagnostic performance of AI models in CT images of COPD patients, aiming to promote the development of related research in the future.

PubMed, Cochrane Library, Web of Science, and Embase were retrieved up to September 1, 2024. The QUADAS-2 evaluation tool was used to assess the quality of the included studies. Meta-analysis of the included researches was performed using Stata18, RevMan 5.4, and Meta-Disc 1.4 software to merge sensitivity, specificity and plot a summary receiver operating characteristic curve (SROC). Heterogeneity was assessed using the Q statistic, and sources of inter-study heterogeneity were explored through meta-regression analysis.

Twenty-two of 3280 identified studies were eligible. Meta-analysis was performed on 15 of these studies, encompassing a total of 22,817 patients for which statistical metrics were reported or could be calculated. Seven studies were based on deep learning (DL) model, three on machine learning (ML) model, and five on DL model with multiple-instance learning (MIL) mechanisms. One study evaluated both DL and ML models. The meta-analysis results showed that the pooled sensitivity of all DL and ML models was 86 % (95 %CI 78-91 %), specificity was 87 % (95 %CI 83-91 %), and area under the curve was 93 % (95 %CI 90-95 %). Subgroup analyses revealed no significant difference in diagnostic sensitivity and specificity between DL and ML models (sensitivity 82 % (95 %CI 76-87 %), 93 % (95 %CI 85-97 %); specificity 87 % (95 %CI 79-91 %), 84 % (95 %CI 79-88 %), and the DL model with MIL (sensitivity 87 % (95 %CI 61-96 %); specificity 89 % (95 %CI 78-95 %) improved the performance of DL model, but this improvement was not statistically significant (p > 0.05).

Both DL and ML models for diagnosing COPD using CT images exhibited high accuracy. There was no significant difference in diagnostic efficacy between the two types of AI models, and the addition of the MIL mechanism may enhance the performance of the DL model.