Artificial intelligence in colonoscopy

Table 6 Magnifying chromoendoscopy

Ref.	Study design	Algorithm type	Dataset	Results
Takemura et al[51]	Partially blinded retrospective study	CAD using HuPAS	134 pit pattern images	Accuracy: 98.5%
Häfner et al[52]	Partially blinded retrospective study	CAD using Dual-Tree Complex Wavelet Transform	484 RGB pit pattern images	Accuracy: 99.59%
Qi et al[53]	Diagnostic model development	CAD using automated imaged analysis	79 colon samples (14 normal, 44 normal tissue adjacent to cancer, 21 malignant)	Automated segmentation achieved precision ratio of 0.69 and match ratio of 0.73

CAD: Computer-aided diagnosis.