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Meta-Analysis
. 2023 Dec 19;18(12):e0294930.
doi: 10.1371/journal.pone.0294930. eCollection 2023.

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

Hangbin Zhang  1 , Xinyu Yang  1 , Ye Tao  1 , Xinyi Zhang  1 , Xuan Huang  1
Affiliations
Meta-Analysis

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis

Hangbin Zhang et al. PLoS One. .

Abstract

Background: Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intelligence (AI) in the field of endoscopy, specifically in diagnosing colorectal lesions. The research was conducted under the supervision of experienced professionals and trainees.

Methods: EMBASE, PubMed, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure (CNKI) database, and other potential databases were surveyed for articles related to the EC with AI published before September 2023. RevMan (5.40), Stata (14.0), and R software (4.1.0) were used for statistical assessment. Studies that measured the accuracy of EC using AI for colorectal lesions were included. Two authors independently assessed the selected studies and their extracted data. This included information such as the country, literature, total study population, study design, characteristics of the fundamental study and control groups, sensitivity, number of samples, assay methodology, specificity, true positives or negatives, and false positives or negatives. The diagnostic accuracy of EC by AI was determined by a bivariate random-effects model, avoiding a high heterogeneity effect. The ANOVA model was employed to determine the more effective approach.

Results: A total of 223 studies were reviewed; 8 articles were selected that included 2984 patients (4241 lesions) for systematic review and meta-analysis. AI assessed 4069 lesions; experts diagnosed 3165 and 5014 by trainees. AI demonstrated high accuracy, sensitivity, and specificity levels in detecting colorectal lesions, with values of 0.93 (95% CI: 0.90, 0.95) and 0.94 (95% CI: 0.73, 0.99). Expert diagnosis was 0.90 (95% CI: 0.85, 0.94), 0.87 (95% CI: 0.78, 0.93), and trainee diagnosis was 0.74 (95% CI: 0.67, 0.79), 0.72 (95% CI: 0.62, 0.80). With the EC by AI, the AUC from SROC was 0.95 (95% CI: 0.93, 0.97), therefore classified as excellent category, expert showed 0.95 (95% CI: 0.93, 0.97), and the trainee had 0.79 (95% CI: 0.75, 0.82). The superior index from the ANOVA model was 4.00 (1.15,5.00), 2.00 (1.15,5.00), and 0.20 (0.20,0.20), respectively. The examiners conducted meta-regression and subgroup analyses to evaluate the presence of heterogeneity. The findings of these investigations suggest that the utilization of NBI technology was correlated with variability in sensitivity and specificity. There was a lack of solid evidence indicating the presence of publishing bias.

Conclusions: The present findings indicate that using AI in EC can potentially enhance the efficiency of diagnosing colorectal abnormalities. As a valuable instrument, it can enhance prognostic outcomes in ordinary EC procedures, exhibiting superior diagnostic accuracy compared to trainee-level endoscopists and demonstrating comparability to expert endoscopists. The research is subject to certain constraints, namely a limited number of clinical investigations and variations in the methodologies used for identification. Consequently, it is imperative to conduct comprehensive and extensive research to enhance the precision of diagnostic procedures.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schematics representation of the study procedures.
Fig 2
Fig 2. Risk of bias evaluation.
Fig 3
Fig 3
(A) The pooled specificity and sensitivity for the AI group. (B) The SROC curve for the AI group.
Fig 4
Fig 4
(A) The pooled specificity and sensitivity of the Expert cohort. (B) The SROC curve for the Expert cohort.
Fig 5
Fig 5
(A) The pooled specificity and sensitivity of the Trainee cohort. (B) The SROC curve for the Trainee cohort.
Fig 6
Fig 6. The SROC curve for AI, expert, and trainee.
Fig 7
Fig 7. Univariate meta-regression and subgroup analyses in colorectal lesion diagnosis.
*P < 0.05, **P < 0.01, ***P < 0.001.
Fig 8
Fig 8. Illustrates the subgroup analysis of several artificial intelligence (AI) technologies.
Fig 9
Fig 9. Deeks’ funnel plot of endocytoscopy using AI in colorectal lesions.
A, AI group. B, expert group. C, trainee group.
See this image and copyright information in PMC

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