Artificial intelligence and machine learning in colorectal cancer

Table 1 Overview of screening studies

Ref.	Objective	Results
Wang et al[19], 2019	Effect of computer aided detection deep learning models on polyps and adenoma detection rates	Increase in adenoma detection rates [29.1% vs 20.3%, P < 0.001] and mean number of identified adenomas per patient [0.53 vs 0.31, P <0.001]; More hyperplastic adenomas (114 vs 52, P < 0.001) and diminutive polyps (185 vs 102, P < 0.001) identified
Nazarian et al[20], 2021	Detection rates of polyp and adenoma with AI vs without AI	Increase in both polyp detection rates (odds ratio [OR] 1.75, 95%CI 1.56-1.96; P < 0.001) as well as adenoma detection rates (OR 1.53, 95%CI 1.32-1.77; P < 0.001)
Johnson et al[23], 2008; Pickhardt et al[24], 2003	Degree to which CTC is effective in detecting asymptomatic colorectal lesions	Reported identification of 90% of patients with asymptomatic adenomas or cancers (≥ 10 mm in diameter) using CT colonography
Grosu et al[25], 2021	Development of machine learning method differentiating between benign and precancerous lesions in average risk asymptomatic patients using CTC	Sensitivity of 82%, specificity of 85% and AUC of 0.91
Song et al[26], 2015	Development of virtual pathological model to assess the suitability of using image high-order differentiations to distinguish colorectal lesions	Improvement of ROC curve (AUC) from 0.74 to 0.85
Blanes-Vidal et al[30], 2019	Algorithms developed to match CE and colonoscopy-identified polyps based on their estimated size, morphology and location as well as utilizing deep convolutional neural networks for automatic colorectal polyp detection	Localization resulted in high sensitivity (97.1%), specificity (93.3%), and accuracy (96.4%) for identifying polyps when compared to the manual process of polyp detection
Kinar et al[35], 2017	AI-assisted prediction model (MeScore®, Calgary, Alberta, Canada) was designed to identify people at high risk for CRC	Revealed a 2.1-fold increase in cancer detection rates when the model is used in combination with FOBT
Gupta et al[36], 2019	Using CellMax (CMx®) platform to detect and isolate circulating tumor cells in peripheral blood samples	A sensitivity and specificity of 80%

AI: Artificial intelligence; AUC: Area under the curve; CTC: Computed tomographic colonography; CT: Computed tomography; CE: Capsule endoscopy; ROC: Receiver operating characteristic.