膝关节骨关节炎的多式模式KL分级从普通的X射线图
Mohammad Khaleel Sallam Ma'aitah1, Abdulkader Helwan2, Abdelrahman Radwan3
1Electrical Engineering / Robotics and Artificial Intelligence Engineering, Faculty of Engineering & Technology, Applied Science Private University, Amman, Jordan.
Journal of X-ray science and technology
|March 17, 2025
概括
这项研究引入了一种新的AI模型,用于通过X射线对膝关节关节炎的严重程度进行分级. 多式联络方法准确地识别了疾病的进展,有助于早期诊断和管理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 整形外科 整形外科 整形外科
背景情况:
- 膝关节骨关节炎是一个全球性的健康问题,没有治愈.
- 早期识别对于控制骨关节炎进展至关重要.
- 用于骨关节炎分级的手动X射线解释是主观的,容易出现错误.
研究的目的:
- 开发一种多式人工智能模型,用于准确的膝关节骨关节炎严重程度分级 (Kellgren-Lawrence分类).
- 利用预先训练有素的视觉和语言模型,从X射线图像和相关文本数据中提取功能.
- 与传统方法相比,提高骨关节炎评估的客观性和准确性.
主要方法:
- 开发了一个多式模式,集成视觉转换器 (ViT) 用于图像嵌入和BERT用于文本嵌入.
- 变压器编码器被用来提取神经网络分类器的独特隐藏状态.
- 该模型使用骨关节炎倡议 (OAI) 数据集进行了训练和验证.
主要成果:
- 多式模式模型在识别膝关节骨关节炎严重程度方面取得了高性能.
- 该模型在测试套件上显示了总体准确度为82.85%.
- 在实验评估中,精度达到84.54%,回忆率为82.89%.
结论:
- 拟议的多式人工智能模型显示了准确的膝关节关节炎分级的巨大潜力.
- 与手动解释相比,这种方法为评估疾病严重程度提供了更客观,更可靠的方法.
- 这些发现支持在医疗诊断中使用先进的人工智能,以改善患者管理.
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