Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging

Table 2 Important studies of computer-aided pathological prediction of colorectal lesions

Ref.	Methods and data	Important results	Limitation and drawback
Tamaki et al[47], 2013	A new combination of local features and sampling tested on 908 NBI images	A recognition rate of 96% for 10-fold cross validation and a rate of 93% for separate data	Without investigation on robustness, motion blur, focus, window size, color bleeding, and highlight areas
Ito et al[48], 2018	Use AlexNet to diagnose cT1b, 190 colorectal lesion images from 41 patient cases	Sensitivity 67.5%, specificity 89.0%, accuracy 81.2%, and AUC 0.871	Insufficient pathological images to build CNNs
Zachariah et al[50], 2020	A CNNs model using TensorFlow and ImageNet, 6223 images with 80% train and 20% test, processing at 77 frames per second	Negative 97% among diminutive rectum/ rectosigmoid polyps, surveillance interval 93%. In fresh validation, NPV 97% and surveillance interval 94%	Retrospective study using offline images
Shahidi et al[51], 2020	An established real-time AI clinical decision support solution to resolve endoscopic and pathologic discrepancies, 644 images with colorectal lesions ≤ 3 mm	CDSS was consistent with the endoscopic diagnosis in 577 (89.6%) lesions	Inevitable CDSS optimization, given the increasingly used deep learning for development of current AI platforms, manifesting in AI’s ability to adapt with increasing data exposure

AI: Artificial intelligence; CNN: Convolutional neural network; AUC: Area under the curve; CDSS: Clinical decision support system.