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.

Table 2 Artificial intelligence and colon capsule endoscopy review

Colon capsule endoscopy	Deep-learning models using convolutional neural networks to better detect colonic abnormalities
Ref.	Model designed to detect
Ribeiro et al[22], 2025	Ulcers and erosions	124 CCE exams; AUC: 1.00; accuracy: 99.6%, sensitivity: 96.9%, specificity: 99.9%; overall accuracy: 99.6%
Mascarenhas et al[23], 2022	Intraluminal blood and mucosal lesions	124 CCE exams; mean sensitivity: 96.3% and specificity: 98.2%; mucosal lesions - sensitivity: 92.0%, specificity: 98.5%; blood - sensitivity: 97.2%, specificity: 99.9%
Mascarenhas et al[24], 2022	Protruding lesions	124 CCE exams; AUC: 0.99; accuracy: 95.3%, sensitivity: 90.0%, specificity: 99.1%, PPV: 98.6%, NPV: 93.2%
Saraiva et al[25], 2021	Protruding lesions	24 CCE exams; AUC: 0.97; sensitivity: 90.7%, specificity: 92.6%, PPV: 79.2%, NPV: 96.9%
Blanes-Videl et al[26], 2019	Polyps, compared to trained endoscopists	250 patients; accuracy: 96.4%, sensitivity: 97.1%, specificity: 93.3%
Gilabert et al[27], 2022	Polyps, and evaluate the reviewing time compared to the classical linear review software	18 studies; reviewing time was reduced by a factor of 6, and polyp detection sensitivity was increased from 81.08% to 87.80%
Yamada et al[28], 2021	Polyps and cancers	15933 CCE images; AUC: 0.902; accuracy: 83.9%, sensitivity: 79.0%, specificity: 87.0%

CCE: Colon capsule endoscopy; AUC: Area under curve; PPV: Positive predictive value; NPV: Negative predictive value.