首页|Department of ECE Reports Findings in Endoscopy (A multi-label dataset and its e valuation for automated scoring system for cleanliness assessment in video capsu le endoscopy)
Department of ECE Reports Findings in Endoscopy (A multi-label dataset and its e valuation for automated scoring system for cleanliness assessment in video capsu le endoscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgical Procedures - Endoscopy is the subject of a report. According to news reporting from New Delhi , India, by NewsRx journalists, research stated, “An automated scoring system fo r cleanliness assessment during video capsule endoscopy (VCE) is presently lacki ng. The present study focused on developing an approach to automatically assess the cleanliness in VCE frames as per the latest scoring i.e., Korea-Canada (KODA ).” The news correspondents obtained a quote from the research from the Department o f ECE, “Initially, an easy-to-use mobile application called artificial intellige nce-KODA (AI-KODA) score was developed to collect a multi-label image dataset of twenty-eight patient capsule videos. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated this dataset in a duplicate manner. T he labels were saved automatically in real-time. Inter-rater and intra-rater rel iability were checked. The developed dataset was then randomly split into train: validate:test ratio of 70:20:10 and 60:20:20. It was followed by a comprehensive benchmarking and evaluation of three multi-label classification tasks using ten machine learning and two deep learning algorithms. Reliability estimation was f ound to be overall good among the three readers. Overall, random forest classifi er achieved the best evaluation metrics, followed by Adaboost, KNeighbours, and Gaussian naive bayes in the machine learning-based classification tasks. Deep le arning algorithms outperformed the machine learning-based classification tasks f or only VM labels. Thorough analysis indicates that the proposed approach has th e potential to save time in cleanliness assessment and is user-friendly for rese arch and clinical use.”
New DelhiIndiaAsiaCyborgsEmergin g TechnologiesEndoscopyHealth and MedicineMachine LearningMinimally Inva sive Surgical ProceduresSurgery
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