Expanding the use of artificial intelligence in non-invasive colorectal cancer screening

Table 1 Artificial intelligence and computed tomography colonography review

Ref.	CT colonography
Song et al[16], 2014	Virtual pathological model exploring texture features to differentiate neoplastic from non-neoplastic lesions	148 colon lesions; AUC: 0.74 (using the image intensity alone) to 0.85 (considering the gradient and curvature images)
Taylor et al[17], 2008	Computer-aided detection software to detect flat early colon carcinoma	24 flat T1 tumors; CAD detected 20 (83.3%), 17 (70.8%), and 13 (54.1%) of the 24 cancers at filter settings of 0, 0.75, and 1
Grosu et al[18], 2025	AI-assisted differentiation of adenomatous and non-adenomatous colorectal polyps as compared to standard radiology reading	59 patients, 118 polyps; AI-assisted readings with higher accuracy (76% ± 1% vs 84% ± 1%), sensitivity (78% ± 6% vs 85% ± 1%), and specificity (73% ± 8% vs 82% ± 2%) in selecting polyps eligible for polypectomy (P < 0.001)
Alkabbany et al[19], 2022	AI-based fusion of 2D projections with 3D colon representation images to generate new synthetic images used to train a RetinaNet model to detect polyps	49 patients, 59 Polyps; 94% f1-score and 97% sensitivity
Endo et al[20], 2025	Deep-learning-based AI algorithm developed to improve the detection of polyps	92 lesions in the interval validation dataset; sensitivity of 0.815, 0.738, and 0.883 for lesions ≥ 6 mm, 6 mm to 10 mm, and ≥ 10 mm, respectively

AUC: Area under curve; CAD: Computer-aided detection; AI: Artificial intelligence; 2D: 2-dimensional; 3D: 3-dimensional.