Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial

Chen, Di; Wu, Lianlian; Li, Yanxia; Zhang, Jun; Li, Jun; Huang, Li; Jiang, Xiaoda; Huang, Xu; Mu, Ganggang; Hu, Shan; Hu, Xiao; Gong, Dexin; He, Xinqi; Yu, Honggang

doi:10.1016/j.gie.2019.09.016

Cited by 65 publications

(56 citation statements)

References 27 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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320

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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

320

215

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“…The field of gastroenterology has been an early leader in the completion of randomized prospective trials evaluating AI technologies, and 5 of these 6 randomized controlled trials are in the field of GI endoscopy. 2,5,[38][39][40] Optimal study design approaches for clinical trials of AI in colonoscopy have been outlined by Vinsard et al, 41 and the recommendations have important implications for GI endoscopy AI trials in general. Clinically relevant outcome measures must be prespecified based on the AI application investigated.…”

Section: Research Prioritiesmentioning

confidence: 99%

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force

Berzin

Parasa

Wallace

et al. 2020

Gastrointestinal Endoscopy

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Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes. (Gastrointest Endosc 2020;92:951-9.) Artificial intelligence (AI) is poised to play a crucial role throughout the practice of gastroenterology. Machine learning (ML) is a branch of AI focusing on computer algorithms that can learn from data and perform specific tasks and analyses, often without explicit human programming. Deep learning is an advanced subset of ML that relies on specific algorithms termed artificial neural networks. The enthusiasm for medical AI applications has centered around expectations that these technologies may assist physicians by assimilating, triaging, and interpreting clinical data (laboratory results, electrocardiographic tracings, CTs, etc) to support physician performance in a consistent manner in all phases of clinical care and automate certain burdensome tasks, such as clinical documentation and billing, to allow physicians to refocus on direct patient care and other meaningful professional activities.

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“…Chen et al[ 47 ] investigated their AI system, ENDOANGEL, which provides prompting of blind spots during upper GI endoscopy, informs the endoscopist of the inspection time and gives a grading score of the percentage of the mucosa that is visualised.…”

Section: Role Of Ai In Quality Control In the Oesophagusmentioning

confidence: 99%

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

Hussein

Puyal

Mountney

et al. 2020

WJG

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The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis. There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus. Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers, therefore curative endoscopic therapy can be offered. Research in this area of artificial intelligence is expanding and the future looks promising. In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.

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Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial

Cited by 65 publications

References 27 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

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

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