Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 2, 2021
Date Accepted: Nov 15, 2021
Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: A pooled analysis of 11685 cases
ABSTRACT
Background:
Artificial intelligence (AI) for gastric cancer diagnosis had been discussed in recent years. Role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice, but to our knowledge, past syntheses appear to have limited focus on the population with early gastric cancer. Therefore, the purpose of this study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) on the diagnosis of early gastric cancer from endoscopic images.
Objective:
Therefore, the purpose of this study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) on the diagnosis of early gastric cancer from endoscopic images.
Methods:
We conducted a systematic review from database inception to June, 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve (AUC) was computed.
Results:
We analyzed 12 retrospective case–control studies (n = 11685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% confidence interval [CI], 0.75–0.92) and 0.90 (95% CI, 0.84–0.93), respectively. The AUC was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results.
Conclusions:
For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI on the diagnosis of early gastric cancer are worthy of future investigation. Clinical Trial: PROSPERO: CRD42020193223
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