Shenzhen Technology University, Shenzhen, China.
Shenzhen GoldenStone Medical Technology Co., Ltd., Shenzhen, China.
Sci Rep. 2024 Jul 19;14(1):16705. doi: 10.1038/s41598-024-67749-5.
Intervertebral Disc Herniation (IVDH) is a common spinal disease in dogs, significantly impacting their health, mobility, and overall well-being. This study initiates an effort to automate the detection and localization of IVDH lesions in veterinary MRI scans, utilizing advanced artificial intelligence (AI) methods. A comprehensive canine IVDH dataset, comprising T2-weighted sagittal MRI images from 213 pet dogs of various breeds, ages, and sizes, was compiled and utilized to train and test the IVDH detection models. The experimental results showed that traditional two-stage detection models reliably outperformed one-stage models, including the recent You Only Look Once X (YOLOX) detector. In terms of methodology, this study introduced a novel spinal localization module, successfully integrated into different object detection models to enhance IVDH detection, achieving an average precision (AP) of up to 75.32%. Additionally, transfer learning was explored to adapt the IVDH detection model for a smaller feline dataset. Overall, this study provides insights into advancing AI for veterinary care, identifying challenges and exploring potential strategies for future development in veterinary radiology.
椎间盘突出症（IVDH）是犬类常见的脊柱疾病，严重影响其健康、活动能力和整体幸福感。本研究旨在利用先进的人工智能（AI）方法，自动检测和定位兽医 MRI 扫描中的 IVDH 病变。我们收集了一个全面的犬类 IVDH 数据集，其中包含了来自 213 只不同品种、年龄和体型的宠物犬的 T2 加权矢状面 MRI 图像，用于训练和测试 IVDH 检测模型。实验结果表明，传统的两阶段检测模型比单阶段模型可靠，包括最近的 You Only Look Once X（YOLOX）探测器。在方法学方面，本研究引入了一种新的脊柱定位模块，成功地集成到不同的目标检测模型中，以增强 IVDH 检测，平均精度（AP）高达 75.32%。此外，还探索了迁移学习来适应较小的猫科动物数据集的 IVDH 检测模型。总的来说，本研究为兽医护理中人工智能的发展提供了深入的见解，确定了挑战，并探讨了兽医放射学未来发展的潜在策略。
