Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning
| Autor: | Jordan Wood, Rajesh N. Keswani, Nikhilesh R. Mazumder, Daniel Byrd, J. Alex Heller, Florencia Garcia Vicente, Matthew William Klug, Anthony D. Yang, Mozziyar Etemadi |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Matching (statistics)
Original article medicine.diagnostic_test business.industry Colonoscopy Cloud computing Machine learning computer.software_genre Identifier Metadata 03 medical and health sciences Upload 0302 clinical medicine Procedure Indication Medicine lcsh:Diseases of the digestive system. Gastroenterology 030211 gastroenterology & hepatology Pharmacology (medical) 030212 general & internal medicine Artificial intelligence lcsh:RC799-869 business Scale (map) computer |
| Zdroj: | Endoscopy International Open Endoscopy International Open, Vol 09, Iss 02, Pp E233-E238 (2021) |
| ISSN: | 2196-9736 2364-3722 |
| Popis: | Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes. |
| Databáze: | OpenAIRE |
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