Automated Detection of Bowel Preparation Scoring and Adequacy With Deep Convolutional Neural Networks
| Autor: | Daniel J Low, Zhuoqiao Hong, Sechiv Jugnundan, Anjishnu Mukherjee, Samir C Grover |
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| Rok vydání: | 2022 |
| Zdroj: | Journal of the Canadian Association of Gastroenterology. 5:256-260 |
| ISSN: | 2515-2092 2515-2084 |
| Popis: | Introduction Adequate bowel preparation is integral to effective colonoscopy. Inadequate bowel preparation has been associated with reduced adenoma detection rate and increased post-colonoscopy colorectal cancer (PCCRC). As a result, the USMSTF recommends early interval reevaluation for colonoscopies with inadequate bowel preparation. However, bowel preparation documentation is highly variable with subjective interpretation. In this study, we developed deep convolutional neural networks (DCNN) to objectively ascertain bowel preparation. Methods Bowel preparation scores were assigned using the Boston Bowel Preparation Scale (BBPS). Bowel preparation adequacy and inadequacy were defined as BBPS ≥2 and BBPS Results The overall accuracy for BBPS subclassification and determination of adequacy was 91% and 98%, respectively. The accuracy for BBPS 0, BBPS 1, BBPS 2, and BBPS 3 was 84%, 91%, 85%, and 96%, respectively. Conclusion We developed DCCNs capable of assessing bowel preparation adequacy and scoring with a high degree of accuracy. However, this algorithm will require further research to assess its efficacy in real-time colonoscopy. |
| Databáze: | OpenAIRE |
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