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

Table 1 Summary of important studies of computer-aided endoscopic colorectal lesion detection

Ref.	Methods and data	Important results	Limitation and drawback
Karkanis et al[40], 2003	Endoscopic video tumor detection by color wavelet covariance, supported by linear discriminant analysis, 66 patients with 95 polyps	Specificity 90% and sensitivity 97%	It is not enough stable to classify different types of colorectal polyps
Misawa et al[41], 2018	An AI-assisted CADe system using 3D CNNs, 155 polyp-positive videos with 391 polyp-negative	Sensitivity 90.0%, specificity 63.3%, and accuracy 76.5%	Further machine and deep learning and prospective evaluations are mandatory
Urban et al[42], 2018	CNNs; 8641 hand-labeled images with 4088 unique polyps	AUC of 0.991 and accuracy of 96.4%	Unknown effects of CNNs on inspection behavior by colonoscopists, anonymous and unidentified natural or endoscopic videos
Mori et al[43], 2018	Retrospective analysis: An AI system by machine learning, 144 diminutive polyps (≤ 5 mm)	Sensitivity 98%, specificity 71%, accuracy 81%, positive 67%, and negative 98%	Insufficient endoscopic video image data
Yamada et al[44], 2020	Retrospective analysis: A deep learning driven system using a Single Shot Multibox Detector for capsule endoscopic colon lesions detection, 15933 training images and 4784 testing images	AUC 0.902, sensitivity 79.0%, specificity 87.0%, accuracy 83.9%, and at a probability cutoff of 0.348	It was a retrospective study that only used the selected images, while it also did not consider pathological diagnoses and the clinical utility of the AI model has not been evaluated

AI: Artificial intelligence; CNN: Convolutional neural network; AUC: Area under the curve.