Artificial intelligence in colonoscopy

Table 8 Confocal endomicroscopy

Ref.	Study design	Algorithm type	Dataset	Results
André et al[58]	Diagnostic model development	CAD using content based image retrieval (CBIR) approach	135 polyps from 71 patients	Accuracy: 89.6%
				Sensitivity 92.5%
				Specificity 83.3%
Ştefănescu et al[59]	Diagnostic model development	CAD using NAVICAD and a two layer CNN	1035 endomicroscopy images including 725 for training, 155 for validation, and 155 for testing.	Testing decision accuracy error rate of 15.48% (24 out of 155 images)
Taunk et al[60]	Feasibility study	CAD using expectation-maximization algorithm	189 endomicroscopy images from 26 patient	Accuracy: 94.2%
				Sensitivity 94.8%
				Specificity 93.5%

CAD: Computer-aided diagnosis; CNN: Convoluted neural network.