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

While MRI is the modality of choice for the diagnosis of longitudinal tears (LTs) of the deep digital flexor tendon (DDFT) of horses, differentiating between various grades of tears based on imaging characteristics is challenging due to overlapping imaging features. In this retrospective, exploratory, diagnostic accuracy study, a machine learning (ML) scheme was applied to link quantitative features and qualitative descriptors to leverage MRI characteristics of different grades of tearing of the DDFT of horses. A qualitative MRI characteristic scheme, combining tendon morphologic features, altered signal intensity, and synovial sheath distention, was used for LT classification with an excellent diagnostic accuracy of the high-grade tears but more limited accuracy for the detection of low-grade tears. A quantitative ML approach was followed to measure the contribution of 30 quantitative phenotypic features for characterizing and classifying tendinous tears. Among the 30 imaging features, boundary curvature represented by the standard deviation and maximum had the most significant discriminatory power (P < 0.05) between normal and abnormal tendons and could be used as an aid for classifying the different grades of LTs of DDFTs. 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 (i.e., margin/distribution and intensity/distribution). 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.