Adenoma Detection Rates Decline with Increasing Procedural Hours in an Endoscopist’s Workload

Almadi, Majid A; Sewitch, Maida; Barkun, Alan N.; Martel, Myriam; Joseph, Lawrence

doi:10.1155/2015/789038

Cited by 30 publications

(24 citation statements)

References 56 publications

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“…Our results differ from other studies which found no association between fatigue and adenoma detection. However, many of these negative studies had smaller sample sizes (400–4,000 colonoscopies), [7, 8, 10–12, 14] which may have limited their power to identify a difference. Another possibility for the discrepancy is differences in physician payment and practice style between settings.…”

Section: Discussionmentioning

confidence: 99%

Adenoma Detection Rate Falls at the End of the Day in a Large Multi-site Sample

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Adenoma Detection Rate Falls at the End of the Day in a Large Multi-site Sample

et al. 2018

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“…However, a diagnosis based on endoscopic findings requires training ( Sugano et al, 2015 , Watanabe et al, 2013 ), is time-consuming and subjective, and may result in false-positive and false-negative results depending on the skill of the endoscopist. Further, fatigue may adversely affect the diagnostic yield of this investigation as shown in a previous report, wherein the adenoma detection rates via colonoscopy declined with increasing procedural hours ( Almadi et al, 2015 ).…”

Section: Introductionmentioning

confidence: 88%

Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images

Shichijo

Nomura

Aoyama³

et al. 2017

EBioMedicine

243

192

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Background and aimsThe role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection.MethodsA 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. pylori (first CNN). Another CNN was trained using images classified according to 8 anatomical locations (secondary CNN). A separate test data set (11,481 images from 397 patients) was evaluated by the CNN, and 23 endoscopists, independently.ResultsThe sensitivity, specificity, accuracy, and diagnostic time were 81.9%, 83.4%, 83.1%, and 198 s, respectively, for the first CNN, and 88.9%, 87.4%, 87.7%, and 194 s, respectively, for the secondary CNN. These values for the 23 endoscopists were 79.0%, 83.2%, 82.4%, and 230 ± 65 min (85.2%, 89.3%, 88.6%, and 253 ± 92 min by 6 board-certified endoscopists), respectively. The secondary CNN had a significantly higher accuracy than endoscopists (by 5.3%; 95% CI, 0.3–10.2).ConclusionH. pylori gastritis could be diagnosed based on endoscopic images using CNN with higher accuracy and in a considerably shorter time compared to manual diagnosis by endoscopists.

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“…21 Furthermore, it has been reported that adenoma detection rates decrease with increasing time devoted to endoscopies because of the fatigue, supporting the idea that computer-aided detection might provide more reliable results. 22 Convolutional neural networks (CNNs) are one of the most popular network architectures of deep learning for images. 11,23 Recent studies showed a promising role for CNNs for the detection or classification of CP during colonoscopy.…”

Section: Introductionmentioning

confidence: 99%

Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks

Ozawa

Ishihara²,

Fujishiro

et al. 2020

Therap Adv Gastroenterol

100

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Background: Recently the American Society for Gastrointestinal Endoscopy addressed the ‘resect and discard’ strategy, determining that accurate in vivo differentiation of colorectal polyps (CP) is necessary. Previous studies have suggested a promising application of artificial intelligence (AI), using deep learning in object recognition. Therefore, we aimed to construct an AI system that can accurately detect and classify CP using stored still images during colonoscopy. Methods: We used a deep convolutional neural network (CNN) architecture called Single Shot MultiBox Detector. We trained the CNN using 16,418 images from 4752 CPs and 4013 images of normal colorectums, and subsequently validated the performance of the trained CNN in 7077 colonoscopy images, including 1172 CP images from 309 various types of CP. Diagnostic speed and yields for the detection and classification of CP were evaluated as a measure of performance of the trained CNN. Results: The processing time of the CNN was 20 ms per frame. The trained CNN detected 1246 CP with a sensitivity of 92% and a positive predictive value (PPV) of 86%. The sensitivity and PPV were 90% and 83%, respectively, for the white light images, and 97% and 98% for the narrow band images. Among the correctly detected polyps, 83% of the CP were accurately classified through images. Furthermore, 97% of adenomas were precisely identified under the white light imaging. Conclusions: Our CNN showed promise in being able to detect and classify CP through endoscopic images, highlighting its high potential for future application as an AI-based CP diagnosis support system for colonoscopy.

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Adenoma Detection Rates Decline with Increasing Procedural Hours in an Endoscopist’s Workload

Abstract: The number of hours devoted to endoscopies before the index colonoscopy was inversely associated with AD rate, with decreased performance possibly as early as within 3 h. This metric should be confirmed in future studies and considered when optimizing scheduling practices.

Cited by 30 publications

References 56 publications

Adenoma Detection Rate Falls at the End of the Day in a Large Multi-site Sample

Adenoma Detection Rate Falls at the End of the Day in a Large Multi-site Sample

Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images

Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks

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