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Key clinical point: Artificial intelligence (AI) shows high sensitivity, specificity, and accuracy for the diagnosis of early gastric cancer.
Major finding: The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 and 0.90, respectively. The accuracy of AI was 0.94. The pooled sensitivity and specificity of deep learning methods were 0.84 and 0.88, respectively, and those of nondeep learning methods were 0.91 and 0.90, respectively. The accuracy of the nondeep learning methods was higher compared with the deep learning methods (0.96 vs. 0.93).
Study details: This meta-analysis of 12 retrospective case-control studies (n = 11,685) assessed the performance of AI in the endoscopic diagnosis of early gastric cancer.
Disclosures: No funding source was identified for this study. The authors declared no conflicts of interest.
Source: Chen P-C et al. The accuracy of artificial intelligence in the endoscopic diagnosis of early gastric cancer: Pooled Analysis Study. J Med Internet Res. 2022;24(5):e27694 (May 16). Doi: 10.2196/27694
Key clinical point: Artificial intelligence (AI) shows high sensitivity, specificity, and accuracy for the diagnosis of early gastric cancer.
Major finding: The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 and 0.90, respectively. The accuracy of AI was 0.94. The pooled sensitivity and specificity of deep learning methods were 0.84 and 0.88, respectively, and those of nondeep learning methods were 0.91 and 0.90, respectively. The accuracy of the nondeep learning methods was higher compared with the deep learning methods (0.96 vs. 0.93).
Study details: This meta-analysis of 12 retrospective case-control studies (n = 11,685) assessed the performance of AI in the endoscopic diagnosis of early gastric cancer.
Disclosures: No funding source was identified for this study. The authors declared no conflicts of interest.
Source: Chen P-C et al. The accuracy of artificial intelligence in the endoscopic diagnosis of early gastric cancer: Pooled Analysis Study. J Med Internet Res. 2022;24(5):e27694 (May 16). Doi: 10.2196/27694
Key clinical point: Artificial intelligence (AI) shows high sensitivity, specificity, and accuracy for the diagnosis of early gastric cancer.
Major finding: The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 and 0.90, respectively. The accuracy of AI was 0.94. The pooled sensitivity and specificity of deep learning methods were 0.84 and 0.88, respectively, and those of nondeep learning methods were 0.91 and 0.90, respectively. The accuracy of the nondeep learning methods was higher compared with the deep learning methods (0.96 vs. 0.93).
Study details: This meta-analysis of 12 retrospective case-control studies (n = 11,685) assessed the performance of AI in the endoscopic diagnosis of early gastric cancer.
Disclosures: No funding source was identified for this study. The authors declared no conflicts of interest.
Source: Chen P-C et al. The accuracy of artificial intelligence in the endoscopic diagnosis of early gastric cancer: Pooled Analysis Study. J Med Internet Res. 2022;24(5):e27694 (May 16). Doi: 10.2196/27694