Colorectal cancer invasion calculation, a colorectal tumor depth diagnostic artificial intelligence, is promising, but its diagnostic accuracy remains a challenge
- PMID: 37340656
- DOI: 10.1111/den.14602
Colorectal cancer invasion calculation, a colorectal tumor depth diagnostic artificial intelligence, is promising, but its diagnostic accuracy remains a challenge
Comment on
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Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data.Dig Endosc. 2023 Jul;35(5):625-635. doi: 10.1111/den.14493. Epub 2023 Jan 18. Dig Endosc. 2023. PMID: 36478234
References
REFERENCES
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- Yao L, Lu Z, Yang G et al. Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data. Dig Endosc 2023; 35: 625-35.
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- Sumimoto K, Tanaka S, Shigita K et al. Clinical impact and characteristics of the narrow-band imaging magnifying endoscopic classification of colorectal tumors proposed by the Japan NBI Expert Team. Gastrointest Endosc 2017; 85: 816-21.
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- Puig I, López-Cerón M, Arnau A et al. Accuracy of the narrow-band imaging international colorectal endoscopic classification system in identification of deep invasion in colorectal polyps. Gastroenterology 2019; 156: 75-87.
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- Takamaru H, Stammers M, Yanagisawa F et al. Conditional inference tree models to perceive depth of invasion in T1 colorectal cancer. Surg Endosc 2022; 36: 9234-43.
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- Lu Z, Xu Y, Yao L et al. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest Endosc 2022; 95: 1186-94.
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