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UID:272@dds.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20221127T130000

DTEND;TZID=Asia/Jerusalem:20221127T133000

DTSTAMP:20221108T071922Z

URL:https://dds.technion.ac.il/iemevents/development-of-a-machine-learning
 -model-to-assess-crohns-disease-severity-from-magnetic-resonance-enterogra
 phy-data/

SUMMARY:Development of a machine-learning model to assess Crohn's disease s
 everity from Magnetic Resonance Enterography data [	\n					Graduate Studen
 t Seminar\n					Seminars\n			\n	]
DESCRIPTION:By: M.Sc. Itai Guez\n Advisors: Prof. Tamir Hazan and Prof. Mot
 i Freiman\n Where: Bloomfield 527 From:\nTechnion\nAbstract:\nRecurrent at
 tentive non-invasive observation of intestinal inflammation is essential f
 or the proper management of Crohn's disease (CD).\n\nThe goal of this stud
 y was to develop and evaluate a multi-modal machine-learning (ML) model to
  assess ileal CD endoscopic activity by integrating information from Magne
 tic Resonance Enterography (MRE) and biochemical biomarkers.\n\nWe obtaine
 d MRE\, biochemical and ileocolonoscopy data from the multi-center ImageKi
 ds study database.\n\nWe developed an optimized multimodal fusion ML model
  to non-invasively assess terminal ileum (TI) endoscopic disease activity 
 in CD from MRE data. We determined the most informative features for model
  development using a permutation feature importance technique.\n\nThe opti
 mized fusion model performed better than the clinically recommended model 
 determined by both a better median test MSE distribution and a better aggr
 egated AUC over the folds.
CATEGORIES:Graduate Student Seminar,Seminars
LOCATION:Bloomfield 527

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TZID:Asia/Jerusalem

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DTSTART:20221030T010000

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