Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis

Figure 1  Flow diagram of the study selection process.

Figure 2 Funnel graph for the assessment of potential publication bias of the included studies. A: Funnel plot for the artificial intelligence-enabled real-time computer-aided detection system group, showing the distribution of logarithm of diagnostic odds ratios log(DOR) values against their standard errors; B: Funnel plot for the endoscopist group, showing the distribution of log(DOR) values against their standard errors. DOR: Diagnostic odds ratio.

Figure 3 Summary quality assessment of studies using Quality Assessment of Diagnostic Accuracy Studies-2 criteria. A: Overall risk of bias and applicability concerns across all included studies; B: Risk of bias and applicability concerns for each individual study.

Figure 4 Forest plots of the pooled sensitivity and specificity for artificial intelligence-enhanced real-time computer-aided detection systems and endoscopist diagnosis. A: Pooled sensitivity for artificial intelligence-enhanced real-time computer-aided detection systems (AI-CAD) group; B: Pooled specificity for AI-CAD group; C: Pooled sensitivity for endoscopist group; D: Pooled specificity for endoscopist group.

Figure 5 Combined summary receiver operating characteristic plot of artificial intelligence-assisted and endoscopist-only diagnosis. SROC: Summary receiver operating characteristic; AUC: Area under the curve.

Figure 6 Forest plots of pooled accuracy difference between artificial intelligence-enhanced real-time computer-aided detection systems and conventional endoscopists across all included studies. Each horizontal line represents the 95% confidence interval (CI) for the accuracy difference in an individual study. A positive value indicates that the Artificial intelligence-enhanced real-time computer-aided detection systems system had higher diagnostic accuracy than endoscopists. The size of the square reflects the weight of each study in the meta-analysis, and the diamond at the bottom represents the pooled effect estimate with its 95%CI, calculated using a random-effects model. CI: Confidence interval.

Figure 7 Forest plots of pooled logarithm of diagnostic odds ratios difference between artificial intelligence-enhanced real-time computer-aided detection systems and conventional endoscopists across all included studies. Each study is represented by a point estimate and a 95% confidence interval (CI). A logarithm of diagnostic odds ratios difference greater than zero indicates a higher diagnostic discriminative ability of Artificial intelligence-enhanced real-time computer-aided detection systems compared to endoscopists. The diamond at the bottom shows the pooled estimate with 95%CI, calculated using a random-effects model. Notably, the wide confidence interval suggests substantial between-study heterogeneity and potential uncertainty in effect direction. CI: Confidence interval; DOR: Diagnostic odds ratio.

Figure 8 Sensitivity analysis of image modality and artificial intelligence model effects. A: Leave-one-out (LOO) sensitivity analysis of artificial intelligence-enhanced real-time computer-aided detection systems (AI-CAD) group specificity, stratified by imaging modality; B: LOO sensitivity analysis of accuracy difference, stratified by imaging modality; C: LOO sensitivity analysis of AI-CAD group specificity, stratified by artificial intelligence (AI) model; D: LOO sensitivity analysis of accuracy difference, stratified by AI model.