A novel artificial intelligence system for the assessment of bowel preparation (with video)

Zhou, Jie; Wu, Lianlian; Wan, Xinyue; Shen, Lianfeng; Li, Jun; Zhang, Jun; Jiang, Xiaoda; Wang, Zhengqiang; Yu, Shijie; Kang, Jian; Li, Ming; Hu, Shan; Hu, Xiao; Gong, Dexin; Chen, Di; Yao, Liwen; Zhu, Yi; Yu, Honggang

doi:10.1016/j.gie.2019.11.026

Cited by 95 publications

(63 citation statements)

References 23 publications

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“…Furthermore, CT is more sensitive and commonly used than X-ray for identifying COVID-19. In our previous work, we succeeded in recruiting deep learning in minor lesion detection and real-time assistance to doctors in gastrointestinal endoscopy [12][13][14][15][16] . Here, we enrolled this technique in identification of COVID-19 pneumonia in CT images.…”

Section: Discussionmentioning

confidence: 99%

“…Deep learning, an important breakthrough in the domain of AI in the past decade, has huge potential at extracting tiny features by the basic unit of DCNN's sampling kernel in image analysis 11 . Our group also succeeded in recruiting this technique in minor lesion detection and real-time assistance to doctors in gastrointestinal endoscopy [12][13][14][15][16] .…”

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confidence: 99%

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Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Chen

Zhang

et al. 2020

Sci Rep

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345

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Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Chen

Zhang

et al. 2020

Sci Rep

Self Cite

345

229

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“…ENDOANGEL (Wuhan EndoAngel Medical Technology Company, Wuhan, China), a CNN-based system developed in 2019, can provide an objective assessment of bowel preparation every 30 seconds during the withdrawal phase of a colonoscopy, achieving a 91.89% accuracy. 45 A recent randomized controlled study used the device to monitor real-time withdrawal speed and colonoscopy withdrawal time and demonstrated significant improvement in adenoma detection rates using ENDOANGEL-assisted colonoscopy versus unassisted colonoscopy (17% vs 8%; odds ratio, 2.18; 95% confidence interval, 1.31-3.62; P Z .0026). 46 The second system, GI Genius (Medtronic, Minneapolis, Minn, USA), is an AI-enhanced endoscopy aid device developed to identify colorectal polyps by providing a visual marker on a live video feed during endoscopic examination.…”

Section: Ai-assisted Endoscopymentioning

confidence: 99%

History of artificial intelligence in medicine

Kaul

Enslin

Gross

2020

Gastrointestinal Endoscopy

462

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“…Although this has not been extensively interrogated, machine learning systems might therefore also aid in improving or monitoring endoscopic quality. 36 These quality assurance (QA) algorithms might serve as a 'referee' for endoscopic quality standards, for example, indicating how much colonic surface area is missed during a pullback, when the mucosal surface needs to be cleaned, or colonic withdrawal speed needs to be slowed down. The argument could be made that this might have a larger impact on clinical outcomes than a detection tool for specific gastrointestinal pathology.…”

Section: Recent Advances In Clinical Practicementioning

confidence: 99%

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Sommen

Groof

Struyvenberg

et al. 2020

Gut

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There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.

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A novel artificial intelligence system for the assessment of bowel preparation (with video)

Cited by 95 publications

References 23 publications

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

History of artificial intelligence in medicine

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

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