Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy

Xià, Jì; Xia, Tian; Pan, Jun; Gao, Fei; Wang, Shuang; Qian, Yang‐Yang; Wang, Heng; Zhao, Jie; Jiang, Xi; Zou, Wenbin; Wang, Yuan‐Chen; Zhou, Wei; Li, Zhao‐Shen; Liao, Zhuan

doi:10.1016/j.gie.2020.05.027

Cited by 46 publications

(28 citation statements)

References 24 publications

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“…Thus, we selected the three most representative lesions in the Department of Gastroenterology for subgroup analysis, which are separately ulcer/erosion (nine studies), GI bleeding (seven studies), and polyps/cancer (seven studies). [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] In total, 69,991,447 WCE images in which there are 15,291,964 lesion WCE images were included. All of those studies are retrospective studies.…”

Section: Describe Summary Of the Resultsmentioning

confidence: 99%

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

Qin

Li²,

Fang

et al. 2021

Surg Endosc

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Background Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE. Methods A search in PubMed, SinoMed, and Web of Science was conducted to collect all original publications about CNN implementation in WCE. Assessment of the risk of bias was performed by Quality Assessment of Diagnostic Accuracy Studies-2 risk list. Pooled sensitivity and specificity were calculated by an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. Results 16 articles with 23 independent studies were included. CNN application to WCE was divided into detection on erosion/ulcer, gastrointestinal bleeding (GI bleeding), and polyps/cancer. The pooled sensitivity of CNN for erosion/ulcer is 0.96 [95% CI 0.91, 0.98], for GI bleeding is 0.97 (95% CI 0.93–0.99), and for polyps/cancer is 0.97 (95% CI 0.82–0.99). The corresponding specificity of CNN for erosion/ulcer is 0.97 (95% CI 0.93–0.99), for GI bleeding is 1.00 (95% CI 0.99–1.00), and for polyps/cancer is 0.98 (95% CI 0.92–0.99). Conclusion Based on our meta-analysis, CNN-dependent diagnosis of erosion/ulcer, GI bleeding, and polyps/cancer approached a high-level performance because of its high sensitivity and specificity. Therefore, future perspective, CNN has the potential to become an important assistant for the diagnosis of WCE.

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Section: Describe Summary Of the Resultsmentioning

confidence: 99%

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

Qin

Li²,

Fang

et al. 2021

Surg Endosc

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Section: Applications Of Ai In Gastroenterologymentioning

confidence: 99%

“…Focusing on capsule endoscopy, in a recent study, magnetically controlled capsule endoscopy imaging was employed to develop a CNN that provides an automatic detection and classification system for gastric lesions[ 58 ]. These lesions included erosions, polyps, ulcers, submucosal tumors, normal mucosa, and xanthomas[ 58 ]. A different study used images generated by a wireless capsule to develop a CNN able to classify the small bowel motility among six distinct intestinal motility events[ 59 ].…”

Section: Applications Of Ai In Gastroenterologymentioning

confidence: 99%

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Christou

Tsoulfas

2021

WJG

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Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

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“…and improved by Zhu et al ., resulting higher sensitivity and specificity than those achieved with endoscopists' visual inspection 40,41 . Furthermore, three research groups recently developed and validated their DL models for recognition of gastric deformities (ulcers, cancer, polyps, erosions) 42–44 …”

Section: The Upper Gi Tract (Overview Table 1)mentioning

confidence: 99%

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy

Sinonquel

Eelbode

Bossuyt

et al. 2020

Digestive Endoscopy

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Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.

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Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy

Cited by 46 publications

References 24 publications

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy

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