Bleeding Detection in Wireless Capsule Endoscopy Image Video Using Superpixel-Color Histogram and a Subspace KNN Classifier

Xing, Xiaohan; Xiao, Jia; Meng, M. Helen

doi:10.1109/embc.2018.8513012

Cited by 34 publications

(14 citation statements)

References 11 publications

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“…The diagnosis of bleeding and bleeding-related diseases is particularly important in SBCE. For the diagnosis of the blood contents, our model achieved a sensitivity of 96.1 %, which was comparable to models in other studies 17 18 . Previous studies on the recognition of angiectasia are limited and only differentiated images of angiectasia from normal images 5 6 .…”

Section: Discussionsupporting

confidence: 81%

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy

Zhang

Shi

Zhang³

et al. 2022

Endoscopy

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Background Further development of deep learning-based artificial intelligence (AI) technology to automatically diagnose multiple abnormalities in small-bowel capsule endoscopy (SBCE) videos is necessary. We aimed to develop an AI model, to compare its diagnostic performance with doctors of different experience levels, and to further evaluate its auxiliary role for doctors in diagnosing multiple abnormalities in SBCE videos. Methods The AI model was trained using 280 426 images from 2565 patients, and the diagnostic performance was validated in 240 videos. Results The sensitivity of the AI model for red spots, inflammation, blood content, vascular lesions, protruding lesions, parasites, diverticulum, and normal variants was 97.8 %, 96.1 %, 96.1 %, 94.7 %, 95.6 %, 100 %, 100 %, and 96.4 %, respectively. The specificity was 86.0 %, 75.3 %, 87.3 %, 77.8 %, 67.7 %, 97.5 %, 91.2 %, and 81.3 %, respectively. The accuracy was 95.0 %, 88.8 %, 89.2 %, 79.2 %, 80.8 %, 97.5 %, 91.3 %, and 93.3 %, respectively. For junior doctors, the assistance of the AI model increased the overall accuracy from 85.5 % to 97.9 % (P < 0.001, Bonferroni corrected), comparable to that of experts (96.6 %, P > 0.0125, Bonferroni corrected). Conclusions This well-trained AI diagnostic model automatically diagnosed multiple small-bowel abnormalities simultaneously based on video-level recognition, with potential as an excellent auxiliary system for less-experienced endoscopists.

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Section: Discussionsupporting

confidence: 81%

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy

Zhang

Shi

Zhang³

et al. 2022

Endoscopy

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“…Most methods utilize a combination of color and texture data, abstracted to a neural network or similar enhancement technique, in order to analyze images. (Pan Xing et al, 2018). For example, Silva and colleagues proposed a classification method that incorporated shape and texture in order to detect polyps with an overall detection rate of 68%.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Endoscopic capsule robot-based diagnosis, navigation and localization in the gastrointestinal tract

Hanscom

Cave

2022

Front. Robot. AI

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The proliferation of video capsule endoscopy (VCE) would not have been possible without continued technological improvements in imaging and locomotion. Advancements in imaging include both software and hardware improvements but perhaps the greatest software advancement in imaging comes in the form of artificial intelligence (AI). Current research into AI in VCE includes the diagnosis of tumors, gastrointestinal bleeding, Crohn’s disease, and celiac disease. Other advancements have focused on the improvement of both camera technologies and alternative forms of imaging. Comparatively, advancements in locomotion have just started to approach clinical use and include onboard controlled locomotion, which involves miniaturizing a motor to incorporate into the video capsule, and externally controlled locomotion, which involves using an outside power source to maneuver the capsule itself. Advancements in locomotion hold promise to remove one of the major disadvantages of VCE, namely, its inability to obtain targeted diagnoses. Active capsule control could in turn unlock additional diagnostic and therapeutic potential, such as the ability to obtain targeted tissue biopsies or drug delivery. With both advancements in imaging and locomotion has come a corresponding need to be better able to process generated images and localize the capsule’s position within the gastrointestinal tract. Technological advancements in computation performance have led to improvements in image compression and transfer, as well as advancements in sensor detection and alternative methods of capsule localization. Together, these advancements have led to the expansion of VCE across a number of indications, including the evaluation of esophageal and colon pathologies including esophagitis, esophageal varices, Crohn’s disease, and polyps after incomplete colonoscopy. Current research has also suggested a role for VCE in acute gastrointestinal bleeding throughout the gastrointestinal tract, as well as in urgent settings such as the emergency department, and in resource-constrained settings, such as during the COVID-19 pandemic. VCE has solidified its role in the evaluation of small bowel bleeding and earned an important place in the practicing gastroenterologist’s armamentarium. In the next few decades, further improvements in imaging and locomotion promise to open up even more clinical roles for the video capsule as a tool for non-invasive diagnosis of lumenal gastrointestinal pathologies.

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“…In the pre-AI era, a proprietary Suspected Blood Indicator (SBI) performance had high sensitivity (96%) but poor specificity (variable 17-65%). 11 Since then, Xing et al 12 proposed a strongly supervised DL method based on the analysis of superpixel-color histograms for classification of 500 frames with blood content and 500 normal frames. It outperformed earlier classifiers with sensitivity, specificity, and accuracy more than 98% and displayed a segmentation of bleeding zones within abnormal frames.…”

Section: Detection Of Lesions and Abnormalitiesmentioning

confidence: 99%

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

Dray

Iakovidis

Houdeville

et al. 2021

J of Gastro and Hepatol

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Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarce, and no prospective studies have been conducted yet. Automated characterization (in terms of diagnosis and pertinence) by supervised machine learning solutions is the next step. It relies on large, thoroughly labeled databases, for which preliminary “ground truth” definitions by experts are of tremendous importance. Other developments are under ways, to assist physicians in localizing anatomical landmarks and findings in the small bowel, in measuring lesions, and in rating bowel cleanliness. It is still questioned whether artificial intelligence will enter the market with proprietary, built‐in or plug‐in software, or with a universal cloud‐based service, and how it will be accepted by physicians and patients.

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Bleeding Detection in Wireless Capsule Endoscopy Image Video Using Superpixel-Color Histogram and a Subspace KNN Classifier

Cited by 34 publications

References 11 publications

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy

Endoscopic capsule robot-based diagnosis, navigation and localization in the gastrointestinal tract

Artificial intelligence in small bowel capsule endoscopy ‐ current status, challenges and future promise

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