Park Junseol, Cho Hyunwoo, Ji Yewon, Lee Kichang, Yoon Hakyoung
Department of Veterinary Medical Imaging, College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea.
Biosafety Research Institute and College of Veterinary Medicine, Jeonbuk National University, Iksan, Republic of Korea.
Front Vet Sci. 2024 Feb 15;11:1334438. doi: 10.3389/fvets.2024.1334438. eCollection 2024.
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.
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.
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.
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.
退行性脊椎病是一种非炎性骨赘反应，其发生是为了重新建立椎间盘之间弱化关节的稳定性。然而，使用X线摄影评估这些变化具有主观性且困难。在人类医学中，已尝试使用人工智能来准确诊断医学影像中疑难和不明确的疾病。深度学习作为人工智能的一种形式，在医学影像数据分析中最为常用。它是一种利用神经网络从数据中自我学习并提取特征以诊断疾病的技术。然而，尚未开发出深度学习模型来检测犬胸腰椎和腰椎侧位X线图像中的脊椎疾病。因此，本研究旨在建立一个能自动识别犬胸腰椎和腰椎侧位X线片中椎体和退行性脊椎病的分割模型。
共使用来自162只犬的265张胸腰椎和腰椎侧位X线图像，基于注意力U-Net算法开发并评估深度学习模型，以分割椎体并检测退行性脊椎病。
在测试数据集中比较深度学习模型和兽医临床医生识别退行性脊椎病的能力时，kappa值为0.839，表明几乎完全一致。
本研究开发的深度学习模型有望自动检测犬胸腰椎和腰椎侧位X线片中的退行性脊椎病，有助于快速准确地识别不稳定的椎间盘间隙部位。此外，本研究开发的分割模型有望用于开发自动识别各种脊椎和椎间盘疾病的模型。
