Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy

Groen, Piet C. de

doi:10.1016/j.tgie.2019.150640

Cited by 11 publications

(8 citation statements)

References 62 publications

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“…All it means is that the endoscopist has met the expectations of the AI-based classifiers. What eventually is needed are trials that show that AI-based techniques implemented during colonoscopy lower the incidence, morbidity and mortality of CRC [20]. Those have been and will continue to be the ultimate indicators of successful CRC prevention; therefore AI assisted systems need to show that their implementation lowers these CRC benchmarks.…”

Section: Discussionmentioning

confidence: 99%

“…Objective measurements of quality of colonoscopy are important to reduce subjective biases and differences among endoscopists [19]. We focus on three key measures of quality of colonoscopy [20]: the amount of blurry (non-informative) images during the withdrawal phase, the quality of bowel preparation by patients prior to colonoscopy and the effort to remove remaining debris by the endoscopist, and the quality of the endoscope navigation inside the colon. The latter remains very challenging to solve, but has recently gained more interest due to its significance to the clinical outcome.…”

Section: Analysis For Objective Quality Measurementsmentioning

confidence: 99%

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Artificial Intelligence for Colonoscopy: Past, Present, and Future

Tavanapong

Riegler

et al. 2022

IEEE J. Biomed. Health Inform.

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During the past decades, many automated image analysis methods have been developed for colonoscopy. Realtime implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.

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Section: Discussionmentioning

confidence: 99%

Section: Analysis For Objective Quality Measurementsmentioning

confidence: 99%

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Tavanapong

Riegler

et al. 2022

IEEE J. Biomed. Health Inform.

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“… 74 , 75 This can be highly valued also in IBD, where the endoscopic assessment can be influenced by operator subjectivity. 76 A computer-aided diagnosis (CAD) system in use with EC was developed to predict persistent histologic inflammation in UC patients. 47 CAD provided good performance measures, showing sensitivity, specificity, and accuracy of 74% (95% CI: 65–81%), 97% (95% CI: 95–99%), and 91% (95% CI: 83–95%), respectively.…”

Section: What Is Next: Capsule Endoscopy Artificial Intelligence and Molecular Imagingmentioning

confidence: 99%

Rediscovering histology: what is new in endoscopy for inflammatory bowel disease?

Solitano

D’Amico

Allocca

et al. 2021

Therap Adv Gastroenterol

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The potential of endoscopic evaluation in the management of inflammatory bowel diseases (IBD) has undoubtedly grown over the last few years. When dealing with IBD patients, histological remission (HR) is now considered a desirable target along with symptomatic and endoscopic remission, due to its association with better long-term outcomes. Consequently, the ability of endoscopic techniques to reflect microscopic findings in vivo without having to collect biopsies has become of upmost importance. In this context, a more accurate evaluation of inflammatory disease activity and the detection of dysplasia represent two mainstay targets for IBD endoscopists. New diagnostic technologies have been developed, such as dye-less chromoendoscopy, endomicroscopy, and molecular imaging, but their real incorporation in daily practice is not yet well defined. Although dye-chromoendoscopy is still recommended as the gold standard approach in dysplasia surveillance, recent research questioned the superiority of this technique over new advanced dye-less modalities [narrow band imaging (NBI), Fuji intelligent color enhancement (FICE), i-scan, blue light imaging (BLI) and linked color imaging (LCI)]. The endoscopic armamentarium might also be enriched by new video capsule endoscopy for monitoring disease activity, and high expectations are placed on the application of artificial intelligence (AI) systems to reduce operator-subjectivity and inter-observer variability. The goal of this review is to provide an updated insight on contemporary knowledge regarding new endoscopic techniques and devices, with special focus on their role in the assessment of disease activity and colorectal cancer surveillance.

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“…Selection of the frames containing abnormalities is also a difficult task because typically only 5% of the frames contain abnormalities [ 8 ]. Several researchers [ 3 , 6 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] have suggested different solutions for the automated detection of stomach diseases. However, due to the resemblance of different symptoms including color, shape, texture, etc., it is challenging to accurately classify the type of infection.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the resemblance of different symptoms including color, shape, texture, etc., it is challenging to accurately classify the type of infection. Most of the previous work was conducted on the detection of a single disease/infection [ 1 , 11 , 16 , 17 , 18 ]. Accurate classification of four significant diseases (gastritis, esophagitis, peptic ulcers, and bleeding) and healthy images using a single framework is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

et al. 2021

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In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.

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Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy

Cited by 11 publications

References 62 publications

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Artificial Intelligence for Colonoscopy: Past, Present, and Future

Rediscovering histology: what is new in endoscopy for inflammatory bowel disease?

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

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