Artificial intelligence alert system based on intraluminal view for colonoscopy intubation
Abstract Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative analysis methods for red-out views. We aimed to develop an artificial intelligence (AI)-based system to assess red-out views during intubation i...
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| Format: | Article |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99725-y |
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| author | Yigeng Huang Suwen Li Syeda Sadia Rubab Junjun Bao Cui Hu Jianglong Hong Xiaofei Ren Xiaochang Liu Lixiang Zhang Jian Huang Huizhong Gan Xiaolan Zhou Jie Cao Dong Fang Zhenwang Shi Huanqin Wang Qiao Mei |
| author_facet | Yigeng Huang Suwen Li Syeda Sadia Rubab Junjun Bao Cui Hu Jianglong Hong Xiaofei Ren Xiaochang Liu Lixiang Zhang Jian Huang Huizhong Gan Xiaolan Zhou Jie Cao Dong Fang Zhenwang Shi Huanqin Wang Qiao Mei |
| author_sort | Yigeng Huang |
| collection | DOAJ |
| description | Abstract Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative analysis methods for red-out views. We aimed to develop an artificial intelligence (AI)-based system to assess red-out views during intubation in colonoscopy. Altogether, 479 colonoscopies performed by 34 colonoscopists were analysed using the proposed semi-supervised AI-based system. We compared the AI-based red-out avoiding scores among novice, intermediate, and experienced colonoscopists. The mean AI-based red-out avoiding scores were compared among groups stratified by expert-rated direct observation of procedure or skill (DOPS)-based tip control assessment results. Both the percentage of actual red-out views (p < 0.001) and AI-based red-out avoiding scores (p < 0.001) were significantly different among the novice, intermediate, and experienced groups. Colonoscopists who scored better on the DOPS-based tip control assessment also performed better on the AI-based red-out avoiding skill assessment. AI-based red-out avoiding score was negatively correlated with actual caecal intubation time and actual red-out percentage. Feedback of red-out avoiding score may help remind endoscopists to perform colonoscopy in an effective and safe manner. This system can be used as an auxiliary tool for colonoscopy training. |
| format | Article |
| id | doaj-art-74ec31f7ebf74b8a93eed4f6c22808cd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-74ec31f7ebf74b8a93eed4f6c22808cd2025-08-20T03:52:23ZengNature PortfolioScientific Reports2045-23222025-04-011511810.1038/s41598-025-99725-yArtificial intelligence alert system based on intraluminal view for colonoscopy intubationYigeng Huang0Suwen Li1Syeda Sadia Rubab2Junjun Bao3Cui Hu4Jianglong Hong5Xiaofei Ren6Xiaochang Liu7Lixiang Zhang8Jian Huang9Huizhong Gan10Xiaolan Zhou11Jie Cao12Dong Fang13Zhenwang Shi14Huanqin Wang15Qiao Mei16State Key Laboratory of Transducer Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityState Key Laboratory of Transducer Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, First People’s Hospital of HefeiDepartment of Gastroenterology, First People’s Hospital of HefeiDepartment of Gastroenterology, The Suzhou Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, The Suzhou Affiliated Hospital of Anhui Medical UniversityDepartment of Gastroenterology, Second People’s Hospital of HefeiDepartment of Gastroenterology, Second People’s Hospital of HefeiState Key Laboratory of Transducer Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Gastroenterology, The First Affiliated Hospital of Anhui Medical UniversityAbstract Mucosal contact of the tip of colonoscopy causes red-out views, and more pressure may result in perforation. There is still a lack of quantitative analysis methods for red-out views. We aimed to develop an artificial intelligence (AI)-based system to assess red-out views during intubation in colonoscopy. Altogether, 479 colonoscopies performed by 34 colonoscopists were analysed using the proposed semi-supervised AI-based system. We compared the AI-based red-out avoiding scores among novice, intermediate, and experienced colonoscopists. The mean AI-based red-out avoiding scores were compared among groups stratified by expert-rated direct observation of procedure or skill (DOPS)-based tip control assessment results. Both the percentage of actual red-out views (p < 0.001) and AI-based red-out avoiding scores (p < 0.001) were significantly different among the novice, intermediate, and experienced groups. Colonoscopists who scored better on the DOPS-based tip control assessment also performed better on the AI-based red-out avoiding skill assessment. AI-based red-out avoiding score was negatively correlated with actual caecal intubation time and actual red-out percentage. Feedback of red-out avoiding score may help remind endoscopists to perform colonoscopy in an effective and safe manner. This system can be used as an auxiliary tool for colonoscopy training.https://doi.org/10.1038/s41598-025-99725-yArtificial intelligenceColonoscopyIntubationFeedback |
| spellingShingle | Yigeng Huang Suwen Li Syeda Sadia Rubab Junjun Bao Cui Hu Jianglong Hong Xiaofei Ren Xiaochang Liu Lixiang Zhang Jian Huang Huizhong Gan Xiaolan Zhou Jie Cao Dong Fang Zhenwang Shi Huanqin Wang Qiao Mei Artificial intelligence alert system based on intraluminal view for colonoscopy intubation Scientific Reports Artificial intelligence Colonoscopy Intubation Feedback |
| title | Artificial intelligence alert system based on intraluminal view for colonoscopy intubation |
| title_full | Artificial intelligence alert system based on intraluminal view for colonoscopy intubation |
| title_fullStr | Artificial intelligence alert system based on intraluminal view for colonoscopy intubation |
| title_full_unstemmed | Artificial intelligence alert system based on intraluminal view for colonoscopy intubation |
| title_short | Artificial intelligence alert system based on intraluminal view for colonoscopy intubation |
| title_sort | artificial intelligence alert system based on intraluminal view for colonoscopy intubation |
| topic | Artificial intelligence Colonoscopy Intubation Feedback |
| url | https://doi.org/10.1038/s41598-025-99725-y |
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