Tu1932 AUTOMATED INSERTION TIME, CECAL INTUBATION, AND WITHDRAWAL TIME DURING LIVE COLONOSCOPY USING CONVOLUTIONAL NEURAL NETWORKS - A VIDEO VALIDATION STUDY

Rombaoa, Christopher; Kalra, Ankush; Dao, Tyler; Requa, James; Ninh, Andrew; Samarasena, Jason; Karnes, William E.

doi:10.1016/j.gie.2019.03.1076

Cited by 8 publications

(2 citation statements)

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“…Using a proprietary algorithm developed by Docbot that reliably identifies the cecum, 17 we identified 411 videos as showing a cecum (subsequently called colonoscopy) and 384 procedures that did not show a cecum (subsequently called sigmoidoscopy). Although the RNN model was trained to generalize in its predictions of endoscopic severity scores for both sigmoidoscopy and colonoscopy procedure types, one of our objectives in this study was to measure the influence of the procedure type on the model's performance.…”

Section: Significance Of Procedures Types To Model Performancementioning

confidence: 99%

Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks

et al. 2021

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BACKGROUND AND AIMS: Endoscopic disease activity scoring in ulcerative colitis (UC) is useful in clinical practice but done infrequently. It is required in clinical trials, where it is expensive and slow because human central readers are needed. A machine learning algorithm automating the process could elevate clinical care and facilitate clinical research. Prior work using single-institution databases and endoscopic still images has been promising. METHODS: Seven hundred and ninety-five full-length endoscopy videos were prospectively collected from a phase 2 trial of mirikizumab with 249 patients from 14 countries, totaling 19.5 million image frames. Expert central readers assigned each full-length endoscopy videos 1 endoscopic Mayo score (eMS) and 1 Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score. Initially, video data were cleaned and abnormality features extracted using convolutional neural networks. Subsequently, a recurrent neural network was trained on the features to predict eMS and UCEIS from individual full-length endoscopy videos. RESULTS: The primary metric to assess the performance of the recurrent neural network model was quadratic weighted kappa (QWK) comparing the agreement of the machine-read endoscopy score with the human central reader score. QWK progressively penalizes disagreements that exceed 1 level. The model's agreement metric was excellent, with a QWK of 0.844 (95% confidence interval, 0.787-0.901) for eMS and 0.855 (95% confidence interval, 0.80-0.91) for UCEIS. CONCLUSIONS: We found that a deep learning algorithm can be trained to predict levels of UC severity from full-length endoscopy videos. Our data set was prospectively collected in a multinational clinical trial, videos rather than still images were used, UCEIS and eMS were reported, and machine learning algorithm performance metrics met or exceeded those previously published for UC severity scores.

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Section: Significance Of Procedures Types To Model Performancementioning

confidence: 99%

Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks

et al. 2021

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“…Piloting of CNN-automated procedure labelling such as colonic intubation time, caecal recognition and withdrawal time on video-recorded colonoscopy illustrates high accuracy when compared with manual recording ( R 2 = 0.995). 88…”

Section: Quality Of Endoscopymentioning

confidence: 99%

Artificial intelligence in luminal endoscopy

Gulati

Emmanuel

Patel

et al. 2020

Clin Med Insights Gastroenterol

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Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett’s, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence–augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence–augmented diagnostic luminal endoscopy into our routine clinical practice.

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