Artificial intelligence in colonoscopy

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Aug 7, 2021; 27(29): 4802-4817
Published online Aug 7, 2021. doi: 10.3748/wjg.v27.i29.4802

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.

Citation: Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27(29): 4802-4817
URL: https://www.wjgnet.com/1007-9327/full/v27/i29/4802.htm
DOI: https://dx.doi.org/10.3748/wjg.v27.i29.4802

Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27(29): 4802-4817 [PMID: 34447227 DOI: 10.3748/wjg.v27.i29.4802]

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