Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model

Zhang, Ding; Shi, Huiying; Zhang, Hao; Meng, Lingjun; Fan, Mengke; Han, Chaoqun; Zhang, Kun; Ming, Fanhua; Xie, Xiaoping; Liu, Hao; Liu, Jun; Lin, Rong; Hou, Xiaohua

doi:10.1053/j.gastro.2019.06.025

Cited by 225 publications

(179 citation statements)

References 24 publications

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“…With the application of artificial intelligence, SMCE system has been able to screen 80–90% of similar images, greatly reducing the burden on doctors. Similar to published studies, doctor's reading time will further shorten with the application of computer‐assisted diagnosis. Image‐processing technologies have also been applied to CE .…”

Section: Discussionsupporting

confidence: 56%

Standing‐type magnetically guided capsule endoscopy versus gastroscopy for gastric examination: multicenter blinded comparative trial

Lai

Wang

Cai

et al. 2019

Digestive Endoscopy

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Aim To compare feasibility and safety after gastrointestinal checkup by standing‐type magnetically controlled capsule endoscopy (SMCE) and conventional gastroscopy. Methods This was a prospective multicenter, blinded study that compared SMCE with gastroscopy in patients from April 2018 to July 2018. All patients first underwent SMCE and then subsequently had gastroscopy with i.v. anesthesia. We calculated the compliance rates of gastric lesion detection by SMCE using gastroscopy as the standard. Capsule retention rate, incidence of adverse events, and patient satisfaction were documented throughout the study. Results One hundred and sixty‐one patients who completed SMCE and gastroscopy were included in the analysis. Positive compliance rate among SMCE and gastroscopy was 92.0% (95% CI: 80.77%–97.78%). Negative compliance rate was 95.5% (89.80%, 98.52%). Moreover, overall compliance rate was 94.41% (89.65%, 97.41%). Sixty‐four pathological outcomes were identified. Of these 64 outcomes, 50 were detected by both procedures. The gastroscopy method neglected seven findings (such as five erosions, one polyp, and one ulcer). Furthermore, SMCE also overlooked seven lesions (i.e. one erosion, two polyps, one atrophy, and three submucosal tumors). Capsule retention or related adverse events were not reported. Conclusion Standing‐type magnetically controlled capsule endoscopy provides equivalent agreement with gastroscopy and may be useful for screening of gastric illnesses without any anesthesia.

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Section: Discussionsupporting

confidence: 56%

Standing‐type magnetically guided capsule endoscopy versus gastroscopy for gastric examination: multicenter blinded comparative trial

Lai

Wang

Cai

et al. 2019

Digestive Endoscopy

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“…CADe system plays an important role in assisting the detection of suspicious lesions, which may improve detection rates in GI endoscopy. Up till now, CADe system has been applied in the detection of gastric cancer, small bowel diseases, and colorectal polyps …”

Section: Current Situation Of Ai‐aided Endoscopic Image Recognitionmentioning

confidence: 99%

“…It is hoped that AI will be able to process a vast array of stored small bowel capsule endoscopy images quickly to reduce the burden on endoscopists and mitigate their different skill levels. Several studies have shown that AI has potential in the automatic identification of small bowel capsule endoscopy images, including celiac disease, intestinal hookworms, and angiectasia . Ding et al found that a CNN‐based auxiliary model assisted in the recognition of small bowel abnormalities using 113 426 569 images from 6970 patients at 77 medical centers.…”

Section: Current Situation Of Ai‐aided Endoscopic Image Recognitionmentioning

confidence: 99%

“…Several studies have shown that AI has potential in the automatic identification of small bowel capsule endoscopy images, including celiac disease, intestinal hookworms, and angiectasia . Ding et al found that a CNN‐based auxiliary model assisted in the recognition of small bowel abnormalities using 113 426 569 images from 6970 patients at 77 medical centers. The model showed a higher level of sensitivity than detection by gastroenterologists in per‐lesion analyses (99.90% vs 76.89%) and significantly shorter per‐patient reading time (5.9 min vs 96.6 min).…”

Section: Current Situation Of Ai‐aided Endoscopic Image Recognitionmentioning

confidence: 99%

“…Unlike endoscopists, whose strength and performance decline over time, AI is indefatigable. For repetitive and time‐consuming tasks, such as capsule endoscopy, AI far outperforms doctors . Moreover, CADm can carry out on the real‐time quality control of the endoscopy, reminding physicians of the standards required in such operation .…”

Section: Future Challenges Of Ai In Gi Endoscopymentioning

confidence: 99%

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Application of artificial intelligence in gastrointestinal endoscopy

et al. 2019

J of Digest Diseases

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With recent significant improvements in artificial intelligence (AI), especially in the field of deep learning, an increasing number of studies have evaluated the use of AI in endoscopy to detect and diagnose gastrointestinal (GI) lesions. The present review summarizes current publications on the use of AI in GI endoscopy and focuses on the challenges and future of AI-aided systems. We expect AI to provide an effective and practical method for endoscopists in lesion detection and characterization as well as in quality control in endoscopy. However, so far, most studies have remained at the preclinical stage. More attention should be paid in the future to the use of AI in reallife clinical applications.Artificial intelligence (AI), first proposed in the 1950s, 1 uses computers to simulate certain thought processes and forms of intelligent behavior. In recent years, one way to achieve AI, namely machine learning, has been used in a variety of medical fields, such as radiology, pathology, ophthalmology, orthopedics, obstetrics, and gynecology, in an attempt to improve physicians' diagnoses. 2 In the field of gastrointestinal (GI) endoscopy endoscopists face heavy workloads, and different kinds of endoscopic images, such as those of esophagogastroduodenoscopy, colonoscopy and capsule endoscopy, has led specialty gastroenterology societies to call for practical and effective methods in order to improve the efficiency and quality of endoscopy in daily clinical practice.With the advent of data-driven deep learning (DL), the inefficient and incomplete feature extraction that results from manual extraction by using traditional machine learning has been thoroughly addressed, and this has revolutionized the research and development of AI. 3,4 After having been trained by a large number of labeled images, the DL algorithm can classify enormous quantities of images. 3 A variety of DL algorithms have thus been created, of which convolutional neural networks (CNN) are the most widely used for medical image recognition.However, DL has some defects, including the requirement of highquality databases and its black-box nature, which have been summarized in Table 1. AI technology used in GI endoscopy falls mainly in two groups:computer-aided detection (CADe) system and computer-aided diagnosis (CADx) system. 5 CADe system is designed to detect and track GI lesions, while CADx system focuses mainly on identifying and characterizing them. Moreover, computer-aided monitoring (CADm) systems are designed for evaluating the examination procedure and † These authors contributed equally to this article.

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Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review

et al. 2022

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Key Points Question Can artificial intelligence be applied in video review of small bowel capsule endoscopy (SBCE)? Findings In this diagnostic study of 5825 patients, a convolutional neural network solution was developed based on CE structured terminology (CEST) to allow a standardized computer-aided detection (CADe) approach. The convolutional neural network was associated with an increased detection rate of SB findings and reduced SBCE video reading times. Meaning This study suggests that a well-structured CADe algorithm, based on CEST, may change the human-based reading and reporting of SBCE videos.

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Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model

Cited by 225 publications

References 24 publications

Standing‐type magnetically guided capsule endoscopy versus gastroscopy for gastric examination: multicenter blinded comparative trial

Standing‐type magnetically guided capsule endoscopy versus gastroscopy for gastric examination: multicenter blinded comparative trial

Application of artificial intelligence in gastrointestinal endoscopy

Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review

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