The impact of reader fatigue on the accuracy of capsule endoscopy interpretation

Beg, Sabina; Card, Timothy R.; Sidhu, Reena; Wrońska, Ewa; Ragunath, Krish; Ching, Hey-Long; Koulaouzidis, Anastasios; Yung, Diana E; Panter, Simon; McAlindon, Mark E.; Johnson, Matthew H.; Kurup, Arun; Shonde, Anthony; Acosta, Miliedis San-Juan; Sansone, Stefano; Simmon, Ebby; Thurston, Victoria; Healy, Ailish; Zammit, Stefania Chetcuti; Schembri, John; Lau, Michelle S; Lam, Ching; Nizamuddin, Mohammed Khaja; Baxter, Anthony D.; Patel, Jay; Archer, Thomas; Oppong, Phillip; Phillips, Frank M.; Dorn, Tara; Fateen, Waleed; White, Jonathan; Budihal, S; Tan, Huey; Tiwari, Rajini

doi:10.1016/j.dld.2021.04.024

Cited by 25 publications

(21 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The task of reviewing full-length SBCE reading is tedious, and CERs spend on average 30 to 120 min to review and interpret complete SBCE examinations encompassing thousands of images [ 13 ]. Moreover, Beg et al showed that reader accuracy declines after reviewing one full-length SBCE recording and that accuracy is also influenced by reading speed [ 14 ]. In 2019, a large multicenter study in China showed that an AI-based algorithm could reduce the mean CE reading time to 6 min per video while maintaining a high sensitivity for abnormalities detection [ 4 ].…”

Section: Discussionmentioning

confidence: 99%

PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy

Leenhardt

Sainz²,

Rondonotti

et al. 2021

JCM

Self Cite

View full text Add to dashboard Cite

Artificial intelligence (AI) has shown promising results in digestive endoscopy, especially in capsule endoscopy (CE). However, some physicians still have some difficulties and fear the advent of this technology. We aimed to evaluate the perceptions and current sentiments toward the use of AI in CE. An online survey questionnaire was sent to an audience of gastroenterologists. In addition, several European national leaders of the International CApsule endoscopy REsearch (I CARE) Group were asked to disseminate an online survey among their national communities of CE readers (CER). The survey included 32 questions regarding general information, perceptions of AI, and its use in daily life, medicine, endoscopy, and CE. Among 380 European gastroenterologists who answered this survey, 333 (88%) were CERs. The mean average time length of experience in CE reading was 9.9 years (0.5–22). A majority of CERs agreed that AI would positively impact CE, shorten CE reading time, and help standardize reporting in CE and characterize lesions seen in CE. Nevertheless, in the foreseeable future, a majority of CERs disagreed with the complete replacement all CE reading by AI. Most CERs believed in the high potential of AI for becoming a valuable tool for automated diagnosis and for shortening the reading time. Currently, the perception is that AI will not replace CE reading.

show abstract

Section: Discussionmentioning

confidence: 99%

PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy

Leenhardt

Sainz²,

Rondonotti

et al. 2021

JCM

Self Cite

View full text Add to dashboard Cite

show abstract

“…Delineation of lesions declines when multiple SBCEs are reviewed in succession. A recently published article, has confirmed that detection of lesions by expert reviewers declines after reviewing the first SBCE risking underreporting of lesions [47]. Artificial intelligence eliminates fatigability and minimizes risk of lesions being missed.…”

Section: Advantages Of Artificial Intelligencementioning

confidence: 96%

Artificial intelligence within the small bowel: are we lagging behind?

Zammit

Sidhu

2022

Current Opinion in Gastroenterology

Self Cite

View full text Add to dashboard Cite

Purpose of reviewThe use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. Recent findingsThe diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician.

show abstract

“…Reading requires the clinicians to pay and maintain attention for a long time without distractions [ 10 ]. A recent study showed that the fatigue of experienced readers significantly affects their accuracy, causing a decrease of 20% in lesion detection after the first CE study [ 11 ]. To shorten reading times, CE readers often increase the frame rate.…”

Section: Introductionmentioning

confidence: 99%

New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times

et al. 2022

Self Cite

View full text Add to dashboard Cite

BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26–340) and 13 min (range 3–85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.

show abstract

The impact of reader fatigue on the accuracy of capsule endoscopy interpretation

Cited by 25 publications

References 31 publications

PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy

PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy

Artificial intelligence within the small bowel: are we lagging behind?

New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times

Contact Info

Product

Resources

About