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More than 20 procedures are necessary to learn small bowel capsule endoscopy: Learning curve pilot study of 535 trainee cases.

Anders Bo NielsenMichael Dam JensenJacob Broder BrodersenJens KjeldsenChristian B LaursenLars KongeStig Borbjerg Laursen
Published in: Endoscopy international open (2024)
Background and study aims The number of procedures needed to acquire a sufficient level of skills to perform an unassisted evaluation of small bowel capsule endoscopy (SBCE) is unknown. We aimed to establish learning curves, diagnostic accuracy, and the number of procedures needed for reviewing small bowel capsule endoscopies unassisted. Methods An expert panel developed a 1-day course including lessons (examination, anatomy, and pathology) and hands-on training. After completing the course, participants received 50 cases in a randomized sequence. An interactive questionnaire about landmarks, findings, and diagnosis followed each case. After submitting the questionnaire, participants received feedback. Data are presented using CUSUM (cumulative sum control chart) learning curves and sensitivity/specificity analyses compared with expert opinions. Results We included 22 gastroenterologists from 11 different Danish hospitals. A total of 535 cases were reviewed (mean: 28; range: 11-50). CUSUM plots demonstrated learning progression for diagnosis and findings during the course, but none of the participants reached a learning plateau with sufficient competencies. The sensitivity for all findings was 65% (95% confidence interval [CI] 0.51-0.82) for the first 20 procedures and 67% (95% CI 0.58-0.73) from case 21 until completion or dropout. The specificity was 63% (95% CI 0.52-0.74) for the first 20 procedures and 57% (95% CI 0.37-0.77) for the rest. Conclusions Our data indicate that learning SBCE may be more difficult than previously recognized due to low discriminative abilities after 20 cases except for the identification of CD. This indicates that 20 SBCE cases may not be sufficient to achieve competency for reviewing SBCE without supervision.
Keyphrases
  • small bowel
  • electronic health record
  • big data
  • healthcare
  • cross sectional
  • machine learning
  • deep learning
  • artificial intelligence
  • medical students