https://scholars.lib.ntu.edu.tw/handle/123456789/607450
標題: | Development and validation of a deep learning-based algorithm for colonoscopy quality assessment | 作者: | Chang Y.-Y PAI-CHI LI RUEY-FENG CHANG Chang Y.-Y Huang S.-P Chen Y.-Y Chang W.-Y Yen H.-H. |
關鍵字: | Colon;Colonoscopy quality;Deep learning | 公開日期: | 2022 | 來源出版物: | Surgical Endoscopy | 摘要: | 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. ? 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124255918&doi=10.1007%2fs00464-021-08993-y&partnerID=40&md5=0752b4d841baff1c4c75c350fc2cff0f https://scholars.lib.ntu.edu.tw/handle/123456789/607450 |
ISSN: | 09302794 | DOI: | 10.1007/s00464-021-08993-y |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。