Detecting medial patellar luxation with ensemble deep convolutional neural network based on a single rear view image of the hindlimb

Abstract

Medial patellar luxation (MPL) is a common orthopedic disease in dogs, which predisposes elderly and small-breed dogs. Unlike in humans, diagnosis in the early course of the disease is challenging because symptoms and joint-pain expression in canines are vague. Herein, we introduced a deep-learning system to diagnose MPL using a single rear-view hindlimb image. We believe that this is the first attempt to build a deep-learning system to diagnose MPL based on image analysis. Notably, 7689 images were collected from 2653 dogs in 30 private animal clinics between July 2021 and July 2022. Model performance was compared with ResNet50, VGG16, VGG19, Inception-V3, and veterinarian performance. For performance comparison, a professional veterinarian with > 10 years of experience selected images of 25 normal dogs and 25 dogs with MPL. The proposed model showed the highest performance, with 92.5% accuracy, whereas human experts showed an average accuracy of 55.2%. Therefore, our model can diagnose MPL using only a single rear-view hindlimb image. Furthermore, to solve the image uncertainty caused by the input image noise, we used a one-class SVM and ensemble learning methods to ensure model robustness. Our study will help diagnose MPL in clinical settings using a single rear-view hindlimb image.