2022 article

Leveraging MRI characterization of longitudinal tears of the deep digital flexor tendon in horses using machine learning

ELKhamary, A. N., Keenihan, E. K., Schnabel, L. V., Redding, W. R., & Schumacher, J. (2022, April 12). VETERINARY RADIOLOGY & ULTRASOUND.

author keywords: equine; feature extraction; longitudinal tear grading; machine learning classifier; tenoscopy
MeSH headings : Animals; Horse Diseases / diagnosis; Horses; Machine Learning; Magnetic Resonance Imaging / veterinary; Retrospective Studies; Tendons / diagnostic imaging
TL;DR: A systematic approach combining quantitative features with qualitative analyses using ML was diagnostically beneficial in MRI characterization and in discriminating between different grades of LTs of the DDFT of horses. (via Semantic Scholar)
UN Sustainable Development Goal Categories
10. Reduced Inequalities (OpenAlex)
Source: Web Of Science
Added: April 25, 2022

Abstract