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

Ahmed N. ELKhamary, Erin K. Keenihan, Lauren V. Schnabel, William R. Redding, Jim Schumacher

Study: The article discusses a retrospective, exploratory, diagnostic accuracy study that applied a machine learning (ML) scheme to link quantitative features and qualitative descriptors to leverage MRI characteristics of different grades of tearing of the deep digital flexor tendon (DDFT) of horses.

Method: The study used a qualitative MRI characteristic scheme, combining tendon morphologic features, altered signal intensity, and synovial sheath distention, for LT classification. A quantitative ML approach was followed to measure the contribution of 30 quantitative phenotypic features for characterizing and classifying tendinous tears.

Results: Among the 30 imaging features, boundary curvature represented by the standard deviation and maximum had the most significant discriminatory power between normal and abnormal tendons. Imaging analysis-based 3D interactive surface plot supports qualitative characterization of different grades of LTs of the DDFT through clearer visualization of the tendon in three dimensions and simple integration of two perspectives features.

Conclusion: The study concludes that 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. The results could set the groundwork to deepen our understanding of imaging characteristics in LTs of the DDFT.

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