DOP13 Artificial Intelligence (AI) in endoscopy - Deep learning for detection and scoring of Ulcerative Colitis (UC) disease activity under multiple scoring systems
| العنوان: | DOP13 Artificial Intelligence (AI) in endoscopy - Deep learning for detection and scoring of Ulcerative Colitis (UC) disease activity under multiple scoring systems |
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| المؤلفون: | Nageshwar R. Duvvur, Aniruddha Singh, Enrico D. Cremonese, Florian Soudan, R Kalapala, J Asselin, Marietta Iacucci, S Nikfal, G Laage, R Monsurate, Milagros L. Henkel, Michael F. Byrne, S Berry, L Canaran, L St-Denis, Hardik Rughwani, James E. East, Simon Travis, Remo Panaccione |
| المصدر: | Journal of Crohn's and Colitis. 15:S051-S052 |
| بيانات النشر: | Oxford University Press (OUP), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.medical_specialty, medicine.diagnostic_test, business.industry, Deep learning, Gastroenterology, General Medicine, medicine.disease, Convolutional neural network, Ulcerative colitis, Endoscopy, Disease activity, Workflow, medicine, Artificial intelligence, Radiology, business, Blue light |
| الوصف: | Background Computer vision & deep learning(DL)to assess & help with tissue characterization of disease activity in Ulcerative Colitis(UC)through Mayo Endoscopic Subscore(MES)show good results in central reading for clinical trials.UCEIS(Ulcerative Colitis Endoscopic Index of Severity)being a granular index,may be more reflective of disease activity & more primed for artificial intelligence(AI). We set out to create UC detection & scoring,in a single tool & graphic user interface(GUI),improving accuracy & precision of MES & UCEIS scores & reducing the time elapsed between video collection,quality assurance & final scoring.We apply DL models to detect & filter scorable frames,assess quality of endoscopic recordings & predict MES & UCEIS scores in videos of patients with UC Methods We leveraged>375,000frames from endoscopy cases using Olympus scopes(190&180Series).Experienced endoscopists & 9 labellers tagged~22,000(6%)images showing normal, disease state(MES orUCEIS subscores)& non-scorable frames.We separate total frames in 3 categories:training(60%),testing(20%)&validation(20%).Using a Convolutional Neural Network(CNN)Inception V3,including a biopsy & post-biopsy detector,an out-of-the-body framework & blue light algorithm.Similar architecture for detection with multiple separate units & corresponding dense layers taking CNN to provide continuous scores for 5 separate outputs:MES,aggregate UCEIS & individual components Vascular Pattern,Bleeding & Ulcers. Results Multiple metrics evaluate detection models.Overall performance has an accuracy of~88% & a similar precision & recall for all classes. MAE(distance from ground truth)& mean bias(over/under-prediction tendency)are used to assess the performance of the scoring model.Our model performs well as predicted distributions are relatively close to the labelled,ground truth data & MAE & Bias for all frames are relatively low considering the magnitude of the scoring scale. To leverage all our models,we developed a practical tool that should be used to improve efficiency & accuracy of reading & scoring process for UC at different stages of the clinical journey. Conclusion We propose a DL approach based on labelled images to automate a workflow for improving & accelerating UC disease detection & scoring using MES & UCEIS scores. Our deep learning model shows relevant feature identification for scoring disease activity in UC patients, well aligned with both scoring guidelines,performance of experts & demonstrates strong promise for generalization.Going forward, we aim to continue developing our detection & scoring tool. With our detailed workflow supported by deep learning models, we have a driving function to create a precise & potentially superhuman level AI to score disease activity |
| تدمد: | 1876-4479 1873-9946 |
| DOI: | 10.1093/ecco-jcc/jjab073.052 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::933688b31709c93ccc08cd51ceef7dd7 https://doi.org/10.1093/ecco-jcc/jjab073.052 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........933688b31709c93ccc08cd51ceef7dd7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 18764479 18739946 |
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| DOI: | 10.1093/ecco-jcc/jjab073.052 |