外文摘要：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. 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). This study was conducted from February 1, 2023, to August 31, 2023. Two RetinaNet models were used in a cascaded manner, with a self-attention module incorporated into the second RetinaNet's classification subnetwork. The first RetinaNet localized the sesamoid bone in the radiographs, while the second RetinaNet graded the severity of sesamoiditis based on the localized region. Model performance was evaluated using the confusion matrix and averThe proposed model demonstrated a promising classification performance with 92.7% accuracy, surpassing the base RetinaNet model. It achieved a mean average precision (mAP) of 81.8%, indicating superior object detection ability. Notably, performance metrics for each severity category showed significant improvement. The proposed deep learning-based method can accurately localize the position of sesamoid bones and grade the severity of sesamoiditis on equine radiographs, providing corresponding confidence scores. This approach has the potential to be deployed in a clinical environment, improving the diagnostic interpretation of metacarpophalangeal (fetlock) joint radiographs in horses. Furthermore, by expanding the training dataset, the model may learn to assist in the diagnosis of pathologies in other skeletal regions of the horse.
外文关键词：deep learning;artificial intelligence;equine;sesamoiditis;radiograph
作者：Guo, Li;Yu, Xinhui;Thair, Anas;Rideout, Andrew;Collins, Andrew;Wang, Z Jane;Hore, Michael
作者单位：Univ British Columbia;Point Point Res Dev;Baker McVeigh & Clements;Hagyard Equine Med Inst
期刊名称：AMERICAN JOURNAL OF VETERINARY RESEARCH
期刊影响因子：0.0
出版年份：2024
出版刊次：85（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台