Veterinary Chiropractic Treatment as a Measure to Prevent the Occurrence of Spondylosis in Boxers

Detection of spondylosis deformans in thoracolumbar and lumbar lateral X-ray images of dogs using a deep learning network

Introduction

Spondylosis deformans is a non-inflammatory osteophytic reaction that develops to re-establish the stability of weakened joints between intervertebral discs. However, assessing these changes using radiography is subjective and difficult. In human medicine, attempts have been made to use artificial intelligence to accurately diagnose difficult and ambiguous diseases in medical imaging. Deep learning, a form of artificial intelligence, is most commonly used in medical imaging data analysis. It is a technique that utilizes neural networks to self-learn and extract features from data to diagnose diseases. However, no deep learning model has been developed to detect vertebral diseases in canine thoracolumbar and lumbar lateral X-ray images. Therefore, this study aimed to establish a segmentation model that automatically recognizes the vertebral body and spondylosis deformans in the thoracolumbar and lumbar lateral radiographs of dogs.

Methods

A total of 265 thoracolumbar and lumbar lateral radiographic images from 162 dogs were used to develop and evaluate the deep learning model based on the attention U-Net algorithm to segment the vertebral body and detect spondylosis deformans.

Results

When comparing the ability of the deep learning model and veterinary clinicians to recognize spondylosis deformans in the test dataset, the kappa value was 0.839, indicating an almost perfect agreement.

Conclusions

The deep learning model developed in this study is expected to automatically detect spondylosis deformans on thoracolumbar and lumbar lateral radiographs of dogs, helping to quickly and accurately identify unstable intervertebral disc space sites. Furthermore, the segmentation model developed in this study is expected to be useful for developing models that automatically recognize various vertebral and disc diseases.

Evidence-Based Complementary and Alternative Orthopedic Veterinary Medicine

The use of complementary and alternative veterinary medicine (CAVM) continues to become more widespread, especially for the management of chronic pain conditions such as canine osteoarthritis. Many patients have comorbidities that preclude traditional medical options, have not adequately responded to conventional therapies, or have owners interested in pursuing a complementary approach. Evidence-based CAVM can serve as a safe and effective adjunct to manage chronic pain conditions. There is growing evidence in the veterinary literature for the use of acupuncture and some herbal supplements in the multimodal management of canine osteoarthritis. The majority of evidence supporting chiropractic is limited to equine and human literature.