Deep learning model shows promise for detecting and grading sesamoiditis in horse radiographs.

Objective: The objective of this study was to develop a robust machine-learning approach for efficient detection and grading of sesamoiditis in horses using radiographs, specifically in data-limited conditions.

Sample: A dataset of 255 dorsolateral-palmaromedial oblique (DLPMO) and dorsomedial-palmarolateral oblique (DMPLO) equine radiographs were retrospectively acquired from Hagyard Equine Medical Institute. These images were anonymized and classified into 3 categories of sesamoiditis severity (normal, mild, and moderate).