2022
DOI: 10.1007/s00464-022-09414-4
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Conditional inference tree models to perceive depth of invasion in T1 colorectal cancer

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Cited by 2 publications
(4 citation statements)
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“…When combined with algorithms for purposes such as recognition of resection methods 110 , this might significantly ease administrative burdens for endoscopists. In the last place, optimizing accuracy of endoscopic assessment of different polyp characteristics could aid in development of more trustworthy clinical decision-making algorithms or prediction models involving specific polyp characteristics 111 112 113 .…”
Section: Discussionmentioning
confidence: 99%
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“…When combined with algorithms for purposes such as recognition of resection methods 110 , this might significantly ease administrative burdens for endoscopists. In the last place, optimizing accuracy of endoscopic assessment of different polyp characteristics could aid in development of more trustworthy clinical decision-making algorithms or prediction models involving specific polyp characteristics 111 112 113 .…”
Section: Discussionmentioning
confidence: 99%
“…of endoscopic assessment of different polyp characteristics could aid in development of more trustworthy clinical decision-making algorithms or prediction models involving specific polyp characteristics [111,112,113]. On the other hand, clinicians should also be aware of the limitations and potential disadvantages of computer-aided polyp diagnosis.…”
Section: Reviewmentioning
confidence: 99%
“…In Japanese clinical practice, the indication for endoscopic resection of colorectal tumors is determined by an algorithm that is a combination of the Japan Narrow Band Imaging Expert Team (JNET) classification and the pit pattern, which are two IEE classifications 2 . Recently, the performance of machine learning models that take into account multimodal information such as macroscopic types in addition to these IEE classifications has been reported 3,4 . The decision on the indication for endoscopic resection must be made in real time.…”
mentioning
confidence: 99%
“…2 Recently, the performance of machine learning models that take into account multimodal information such as macroscopic types in addition to these IEE classifications has been reported. 3,4 The decision on the indication for endoscopic resection must be made in real time. Hence, these models were implemented as tree-based models that can be mentally calculated by the endoscopist.…”
mentioning
confidence: 99%