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

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.