Yuan-Yen Chang1, Pai-Chi Li1, Ruey-Feng Chang1,2,3, Yu-Yao Chang4, Siou-Ping Huang5, Yang-Yuan Chen5, Wen-Yen Chang6, Hsu-Heng Yen7,8,9,10,11. 1. Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. 2. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. 3. Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan. 4. Department of Colorectal Surgery, Changhua Christian Hospital, Changhua, Taiwan. 5. Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan. 6. Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan. 7. Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan. 91646@cch.org.tw. 8. Department of Colorectal Surgery, Changhua Christian Hospital, Changhua, Taiwan. 91646@cch.org.tw. 9. Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan. 91646@cch.org.tw. 10. Department of Electrical Engineering, Chung Yuan University, Taoyuan, Taiwan. 91646@cch.org.tw. 11. Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan. 91646@cch.org.tw.
Abstract
BACKGROUND: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure. METHODS: A deep learning system for classifying colonoscopy images for quality assurance purposes was developed and its performance was assessed with an independent dataset. The system was utilized to analyze captured images and results were compared with those of real-world reports. RESULTS: In total, 10,417 images from the hospital endoscopy database and 3157 from Hyper-Kvasir open dataset were utilized to develop the quality assurance algorithm. The overall accuracy of the algorithm was 96.72% and that of the independent test dataset was 94.71%. Moreover, 761 real-world reports and colonoscopy images were analyzed. The accuracy of electronic reports about cecal intubation rate was 99.34% and that of the algorithm was 98.95%. The agreement rate for the assessment of polypectomy rates using the electronic reports and the algorithm was 0.87 (95% confidence interval 0.83-0.90). A good correlation was found between the withdrawal time calculated using the algorithm and that entered by the physician (correlation coefficient r = 0.959, p < 0.0001). CONCLUSION: We proposed a novel deep learning-based algorithm that used colonoscopy images for quality assurance purposes. This model can be used to automatically assess intra-procedural colonoscopy quality indicators in clinical practice.
BACKGROUND: Quality indicators should be assessed and monitored to improve colonoscopy quality in clinical practice. Endoscopists must enter relevant information in the endoscopy reporting system to facilitate data collection, which may be inaccurate. The current study aimed to develop a full deep learning-based algorithm to identify and analyze intra-procedural colonoscopy quality indicators based on endoscopy images obtained during the procedure. METHODS: A deep learning system for classifying colonoscopy images for quality assurance purposes was developed and its performance was assessed with an independent dataset. The system was utilized to analyze captured images and results were compared with those of real-world reports. RESULTS: In total, 10,417 images from the hospital endoscopy database and 3157 from Hyper-Kvasir open dataset were utilized to develop the quality assurance algorithm. The overall accuracy of the algorithm was 96.72% and that of the independent test dataset was 94.71%. Moreover, 761 real-world reports and colonoscopy images were analyzed. The accuracy of electronic reports about cecal intubation rate was 99.34% and that of the algorithm was 98.95%. The agreement rate for the assessment of polypectomy rates using the electronic reports and the algorithm was 0.87 (95% confidence interval 0.83-0.90). A good correlation was found between the withdrawal time calculated using the algorithm and that entered by the physician (correlation coefficient r = 0.959, p < 0.0001). CONCLUSION: We proposed a novel deep learning-based algorithm that used colonoscopy images for quality assurance purposes. This model can be used to automatically assess intra-procedural colonoscopy quality indicators in clinical practice.
Authors: Erik A Holzwanger; Mohammad Bilal; Christopher G Stallwood; Mark J Sterling; Robert F Yacavone Journal: Endoscopy Date: 2020-08-26 Impact factor: 10.093
Authors: James P C Ryan; Olwyn Lynch; Mark P Broe; Niall Swan; Diarmaid Moran; Barry McGuire; David Mulvin Journal: Ir J Med Sci Date: 2021-02-27 Impact factor: 1.568