Detection of elusive polyps using a large-scale artificial intelligence system (with videos)
| Autor: | Ehud Rivlin, Liran Katzir, Eran Goldin, Yochai Blau, Jesse Lachter, David Ben Shimol, Yossi Matias, Danny Veikherman, Greg S. Corrado, Tomer Golany, Valentin Dashinsky, Nadav Rabani, Ilan Shimshoni, Daniel Freedman, Amit Aides, Yun Liu, Dan M. Livovsky, Ori Segol |
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| Rok vydání: | 2021 |
| Předmět: |
Validation study
medicine.medical_specialty Artificial Intelligence System Colorectal cancer Colonoscopy Colonic Polyps Adenomatous Polyps Primary outcome Artificial Intelligence otorhinolaryngologic diseases Medicine Humans Radiology Nuclear Medicine and imaging Prospective Studies neoplasms Miss rate medicine.diagnostic_test business.industry Gastroenterology medicine.disease digestive system diseases surgical procedures operative Radiology CRITERION STANDARD business Colorectal Neoplasms |
| Zdroj: | Gastrointestinal endoscopy. 94(6) |
| ISSN: | 1097-6779 0469-3078 |
| Popis: | Background and aims Colorectal cancer is a leading cause of death. Colonoscopy is the criterion standard for detection and removal of precancerous lesions and has been shown to reduce mortality. The polyp miss rate during colonoscopies is 22% to 28%. DEEP DEtection of Elusive Polyps (DEEP2) is a new polyp detection system based on deep learning, which alerts the operator in real-time to the presence and location of polyps. The primary outcome was the performance of DEEP2 on the detection of elusive polyps. Methods The DEEP2 system was trained on 3,611 hours of colonoscopy videos derived from 2 sources, and was validated on a set comprising 1,393 hours, from a third unrelated source. The ground truth labeling was provided by offline gastroenterologist annotators, who were able to watch the video in slow-motion and pause/rewind as required. To assess the applicability, stability, user experience and in order to obtain some preliminary data on performance in a real-life scenario, a preliminary prospective clinical validation study was performed, comprising 100 procedures (clinicaltrial.gov ID: NCT04693078). Results DEEP2 achieved a sensitivity of 97.1% at 4.6 false alarms per video for all polyps, 88.5% and 84.9% for polyps that are in the field of view for less than 5 and 2 seconds, respectively. DEEP2 was able to detect polyps, not seen by live real-time endoscopists or offline annotators in an average of 0.22 polyps per sequence. In the clinical validation study the system detected an average of 0.89 additional polyps per procedure. No adverse events occurred. Conclusions DEEP2 has a high sensitivity for polyp detection and was effective in increasing the detection of polyps both in colonoscopy videos and in real procedures with a low number of false alarms. |
| Databáze: | OpenAIRE |
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