Impact of an artificial intelligence‐aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study

Yamaguchi, Daisuke; Shimoda, Ryo; Miyahara, Koichi; Yukimoto, Takahiro; Sakata, Yasuhisa; Takamori, Ayako; Mizuta, Yumi; Fujimura, Yutaro; Inoue, Suma; Tomonaga, Michito; Ogino, Yousuke; Eguchi, Koichi; Ikeda, Kei; Tanaka, Yuichiro; Takedomi, Hironobu; Hidaka, Hidenori; Akutagawa, Takashi; Tsuruoka, Nanae; Noda, Takahiro; Tsunada, Seiji; Esaki, Motohiro

doi:10.1111/den.14573

Cited by 17 publications

(6 citation statements)

References 33 publications

(81 reference statements)

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“…Polyp detection (CADe) for the CAD EYE system has been evaluated in a recent study by Neumann et al, which showed that it achieved 100% sensitivity in polyp detection and a negligible false-positive frame rate 14 . Its impact on the quality of endoscopies of trainee gastroenterologists has been highlighted in a study by Yamaguchi et al, where the adenoma miss rate was decreased in the CAD EYE group; however, the authors noted no improvement in adenoma or polyp detection rate 22 .…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study

De Lange,

Prouvost,

Rahmi

et al. 2024

Endosc Int Open

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Background and study aims: Optical diagnosis poses challenges to the implementation of "resect and discard" strategies. This study aimed to assess the feasibility and performance of a new commercially available system for colorectal polyps. Patients and Methods: Nine expert endoscopists in three centres performed colonoscopies using AI-equipped colonoscopes (CAD EYE, Fujifilm). Histology and predictions were compared, with hyperplastic polyps and sessile serrated lesions grouped for analysis. Results: Overall, 253 polyps in 119 patients were documented (n=152 adenomas, n=78 hyperplastic polyps, n=23 sessile serrated lesions). CAD EYE detected polyps before endoscopists in 81/253 (32%) of cases. The mean polyp size was 5.5 mm (SD 0.6 mm). Polyp morphology was Paris Ip (4 %), Is (28 %), IIa (60 %), and IIb (8 %). CAD EYE achieved a sensitivity of 80%, specificity of 83%, PPV of 96%, and NPV of 72%. Expert endoscopists had a sensitivity of 88%, specificity of 83%, PPV of 96%, and NPV of 72%. Diagnostic accuracy was similar between CAD EYE (81%) and endoscopists (86%). However, sensitivity was greater with endoscopists as compared to CAD EYE (p<0.05). CAD EYE classified sessile serrated lesions as hyperplasia in 22/23 cases, and endoscopists correctly classified 16/23 cases. Conclusions: The CAD EYE system shows promise for detecting and characterising colorectal polyps. Larger studies are however needed to confirm these findings.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study

De Lange,

Prouvost,

Rahmi

et al. 2024

Endosc Int Open

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“…However, after about 200 experiences of cases, these rates for the last 50 lesions increased 88.0%/95.2% for non-experts. Another study examined the efficacy of CAD EYE for lesion detection by 6 trainees with experience of ≤20 colonoscopies[ 22 ]. The ADR between observation with CAD EYE group and standard observation group were not significant (58.4% vs. 61.0%, p=0.690).…”

Section: Discussionmentioning

confidence: 99%

Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets

Kobayashi,

Yoshida,

Tomita

et al. 2024

J Anus Rectum Colon

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Objectives: Detailed superiority of CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for polyp detection/diagnosis, compared to endoscopists is not well examined. We examined endoscopist's ability using movie sets of colorectal lesions which were detected and diagnosed by CAD EYE accurately. Methods: Consecutive lesions of ≤10 mm were examined live by CAD EYE from March-June 2022 in our institution. Short unique movie sets of each lesion with and without CAD EYE were recorded simultaneously using two recorders for detection under white light imaging (WLI) and linked color imaging (LCI) and diagnosis under blue laser/light imaging (BLI). Excluding inappropriate movies, 100 lesions detected and diagnosed with CAD EYE accurately were evaluated. Movies without CAD EYE were evaluated first by three trainees and three experts. Subsequently, movies with CAD EYE were examined. The rates of accurate detection and diagnosis were evaluated for both movie sets. Results: Among 100 lesions (mean size: 4.7±2.6 mm; 67 neoplastic/33 hyperplastic), mean accurate detection rates of movies without or with CAD EYE were 78.7%/96.7% under WLI (p<0.01) and 91.3%/97.3% under LCI (p<0.01) for trainees and 85.3%/99.0% under WLI (p<0.01) and 92.6%/99.3% under LCI (p<0.01) for experts. Mean accurate diagnosis rates of movies without or with CAD EYE for BLI were 85.3%/100% for trainees (p<0.01) and 92.3%/100% for experts (p<0.01), respectively. The significant risk factors of not-detected lesions for trainees were right-sided, hyperplastic, not-reddish, in the corner, halation, and inadequate bowel preparation. Conclusions: Unique movie sets with and without CAD EYE could suggest it's efficacy for lesion detection/diagnosis.

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“…However, eight of these 15 trials reported a significant improvement for a secondary endpoint. [21][22][23][24][25][26][27][28]…”

Section: What Models Are Being Deployed?mentioning

confidence: 99%

Randomized Controlled Trials Evaluating AI in Clinical Practice: A Scoping Evaluation

Han,

Acosta,

Shakeri

et al. 2023

Preprint

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BackgroundArtificial intelligence (AI) has emerged as a promising tool in healthcare, with numerous studies indicating its potential to perform as well or better than clinicians. However, a considerable portion of these AI models have only been tested retrospectively, raising concerns about their true effectiveness and potential risks in real-world clinical settings.MethodsWe conducted a systematic search for randomized controlled trials (RCTs) involving AI algorithms used in various clinical practice fields and locations, published between January 1, 2018, and August 18, 2023. Our study included 84 trials and focused specifically on evaluating intervention characteristics, study endpoints, and trial outcomes, including the potential of AI to improve care management, patient behavior and symptoms, and clinical decision-making.ResultsOur analysis revealed that 82·1% (69/84) of trials reported positive results for their primary endpoint, highlighting AI’s potential to enhance various aspects of healthcare. Trials predominantly evaluated deep learning systems for medical imaging and were conducted in single-center settings. The US and China had the most trials, with gastroenterology being the most common field of study. However, we also identified areas requiring further research, such as multi-center trials and diverse outcome measures, to better understand AI’s true impact and limitations in healthcare.ConclusionThe existing landscape of RCTs on AI in clinical practice demonstrates an expanding interest in applying AI across a range of fields and locations. While most trials report positive outcomes, more comprehensive research, including multi-center trials and diverse outcome measures, is essential to fully understand AI’s impact and limitations in healthcare.

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Impact of an artificial intelligence‐aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study

Cited by 17 publications

References 33 publications

Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study

Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study

Detailed Superiority of the CAD EYE Artificial Intelligence System over Endoscopists for Lesion Detection and Characterization Using Unique Movie Sets

Randomized Controlled Trials Evaluating AI in Clinical Practice: A Scoping Evaluation

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