Abstract
Background and study aims We evaluated use of artificial intelligence (AI) assisted image classifier in determining
the feasibility of curative endoscopic resection of large colonic lesion based on
non-magnified endoscopic images
Methods AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic
lesions. The independent validation set consisted of 567 endoscopic images from 76
colonic lesions. Histology of the resected specimens was used as gold standard. Curative
endoscopic resection was defined as histology no more advanced than well-differentiated
adenocarcinoma, ≤ 1 mm submucosal invasion and without lymphovascular invasion, whereas
non-curative resection was defined as any lesion that could not meet the above requirements.
Performance of the trained AI image classifier was compared with that of endoscopists.
Results In predicting endoscopic curative resection, AI had an overall accuracy of 85.5 %.
Images from narrow band imaging (NBI) had significantly higher accuracy (94.3 % vs
76.0 %; P < 0.00001) and area under the ROC curve (AUROC) (0.934 vs 0.758; P = 0.002) than images from white light imaging (WLI). AI was superior to two junior
endoscopists in terms of accuracy (85.5 % vs 61.9 % or 82.0 %, P < 0.05), AUROC (0.837 vs 0.638 or 0.717, P < 0.05) and confidence level (90.1 % vs 83.7 % or 78.3 %, P < 0.05). However, there was no statistical difference in accuracy and AUROC between
AI and a senior endoscopist.
Conclusions The trained AI image classifier based on non-magnified images can accurately predict
probability of curative resection of large colonic lesions and is better than junior
endoscopists. NBI images have better accuracy than WLI for AI prediction.
