Performance measures for small-bowel endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative

Spada, Cristiano; McNamara, Deirdre; Despott, Edward J.; Adler, Samuel N.; Cash, Brooks D.; Fernández-Urién, Ignacio; Iveković, Hrvoje; Keuchel, Martin; McAlindon, Mark E.; Saurin, Jean‐Christophe; Panter, Simon; Bellisario, Cristina; Minozzi, Silvia; Senore, Carlo; Bennett, Cathy; Bretthauer, Michael; Dinis-Ribeiro, Mário; Domagk, Dirk; Hassan, Cesare; Kaminski, Michal F.; Rees, Colin; Valori, Roland; Bisschops, Raf; Rutter, Matthew D.

doi:10.1055/a-0889-9586

Cited by 96 publications

(109 citation statements)

References 123 publications

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“…To develop standards for high quality in gastrointestinal endoscopy, the European Society of Gastrointestinal Endoscopy (ESGE) has established the ESGE Quality Improvement Committee, which involved several gastrointestinal endoscopy experts to set up quality indicators 9 10 11 12 that, when met, reflect a high-quality examination for different endoscopic procedures. The ESGE Quality Improvement Committee recently appointed a Small Bowel Working Group (ESBWG) to identify a list of performance measures for small bowel endoscopy, including small bowel capsule endoscopy (SBCE) 13 , which has unique technical features. The ESBWG identified six key quality performance measures and four minor performance measures.…”

Section: Introductionmentioning

confidence: 99%

Quality performance measures for small capsule endoscopy: Are the ESGE quality standards met?

Rondonotti

Spada

Cadoni³

et al. 2021

Endosc Int Open

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Background and study aims The European Society of Gastrointestinal Endoscopy (ESGE) recently issued a quality performance measures document for small bowel capsule endoscopy (SBCE). The aim of this nationwide survey was to explore SBCE practice with ESGE quality measures as a benchmark. Patients and methods A dedicated per-center semi-quantitative questionnaire based on ESGE performance measures for SBCE was created by a group of SBCE experts. One-hundred-eighty-one centers were invited to participate and were asked to calculate performance measures for SBCE performed in 2018. Data were compared with 10 ESGE quality standards for both key and minor performance measures. Results Ninety-one centers (50.3 %) participated in the data collection. Overall in the last 5 years (2014–2018), 26,615 SBCEs were performed, 5917 of which were done in 2018. Eighty percent or more of the participating centers reached the minimum standard established by the ESGE Small Bowel Working Group (ESBWG) for four performance measures (indications for SBCE, complete small bowel evaluation, diagnostic yield and retention rate). Conversely, compliance with six minimum standards established by ESBWG concerning adequate bowel preparation, patient selection, timing of SBCE in overt bleeding, appropriate reporting, reading protocols and referral to device-assisted enteroscopy was met by only 15.5 %, 10.9 %, 31.1 %, 67.7 %, 53.4 %, and 32.2 % of centers, respectively. Conclusions The present survey shows significant variability across SBCE centers; only four (4/10: 40 %) SBCE procedural minimum standards were met by a relevant proportion of the centers ( ≥ 80 %). Our data should help in identifying target areas for quality improvement programs in SBCE.

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Section: Introductionmentioning

confidence: 99%

Quality performance measures for small capsule endoscopy: Are the ESGE quality standards met?

Rondonotti

Spada

Cadoni³

et al. 2021

Endosc Int Open

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“…Our own data (kappa 0.52 -0.71) confirmed this drawback [3]. Thus, a quality initiative process by the ESGE and UEG recently called for "the development of software for assessment of the quality of smallbowel preparation" [4]. Previous studies based on still frames have demonstrated the high diagnostic performances of handcrafted computerized algorithms in assessing small-bowel cleanliness during capsule endoscopy [11,13].…”

Section: Discussionmentioning

confidence: 68%

A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy

et al. 2020

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Background and Aims. Cleanliness scores in small bowel (SB) capsule endoscopy (CE) have poor reproducibility. The aim of this study was to evaluate a neural network (NN)-based algorithm for automated assessment of the SB cleanliness during CE. Methods: First, 600 normal third-generation SBCE still frames were categorized as “adequate” or “inadequate” in terms of cleanliness by three expert readers, according to a 10-point scale and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as “adequate” or “inadequate” in terms of cleanliness. This testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm. Results: Using a threshold of 79% adequate still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3%, a specificity of 83.3%, and an accuracy of 89.7%. The reproducibility was perfect. The mean calculation time per video was 3 ± 1 minutes. Conclusion: This NN-based algorithm allowing automatic assessment of SB cleanliness during CE was highly sensitive and paves the way for automated, standardized SBCE reports.

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“…A consensus standard for technologies in colonoscopy that predict polyp histology (NPV ≥ 90%) was published by the American Society for Gastrointestinal Endoscopy in 2011 30 . We are not aware of an analogous consensus document for WCE but a recently published quality improvement initiative by the European Society for Gastrointestinal Endoscopy recommends a diagnostic yield of at least 50% for all capsule endoscopy readers across indications 31 . Here, we review the literature to date on deep learning in WCE.…”

Section: Artificial Intelligence In Wireless Capsule Endoscopy—an Evomentioning

confidence: 99%

Artificial intelligence and deep learning for small bowel capsule endoscopy

Trasolini

Byrne

2020

Digestive Endoscopy

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Capsule endoscopy is ideally suited to artificial intelligencebased interpretation given its reliance on pattern recognition in still images. Time saving viewing modes and lesion detection features currently available rely on machine learning algorithms, a form of artificial intelligence. Current software necessitates close human supervision given poor sensitivity relative to an expert reader. However, with the advent of deep learning, artificial intelligence is becoming increasingly reliable and will be increasingly relied upon. We review the major advances in artificial intelligence for capsule endoscopy in recent publications and briefly review artificial intelligence development for historical understanding. Importantly, recent advancements in artificial intelligence have not yet been incorporated into practice and it is immature to judge the potential of this technology based on current platforms. Remaining regulatory and standardization hurdles are being overcome and artificial intelligence-based clinical applications are likely to proliferate rapidly.

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Performance measures for small-bowel endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative

Cited by 96 publications

References 123 publications

Quality performance measures for small capsule endoscopy: Are the ESGE quality standards met?

Quality performance measures for small capsule endoscopy: Are the ESGE quality standards met?

A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy

Artificial intelligence and deep learning for small bowel capsule endoscopy

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