一个基于计算机断层扫描的骨折预测模型,使用深度学习:开发和验证研究的脊椎骨和肌肉图像
Sung Hye Kong1, Wonwoo Cho2, Sung Bae Park3
1Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Journal of medical Internet research
|July 12, 2024
概括
机遇式计算机断层扫描 (CT) 扫描可以识别骨折风险. 使用脊椎骨和肌肉图像的新模型准确地预测骨折,优于仅骨和临床模型.
科学领域:
- 放射学 放射学是一门学科.
- 老年病的医生 老年病的医生
- 人工智能的人工智能
背景情况:
- 人口老龄化正在增加,导致更多的机会性计算机断层扫描 (CT) 扫描.
- 脑电图扫描提供了有关老年人骨和肌肉健康的宝贵数据.
研究的目的:
- 开发和验证基于CT的模型,使用脊椎骨和肌肉成像来预测骨折.
- 评估这些模型在五年内对脊椎骨折事件的预测准确度.
主要方法:
- 一项对1214名患者 (2010-2019) 的回顾性纵向队列研究,用于模型开发.
- 在495名患者的外部验证.
- 用一个注意力卷积神经网络-反复的神经网络模型与脊椎骨和椎肌肉图像一起使用.
主要成果:
- 在外部验证中,结合的脊椎骨和肌肉模型实现了优异的预测准确性 (AUROC 0.827) 与仅骨图像 (AUROC 0.815) 相比.
- CT图像模型的表现优于传统的骨折风险评估模型.
- 图像模型显著优于临床模型 (AUROC 0.749).
结论:
- 一个基于CT的模型,包括脊椎骨和椎肌肉图像,显示出优异的骨折预测性能.
- 机会性CT查有可能识别未来高骨折风险的个体.
关键词:
骨头 骨头 骨头 骨头骨头 骨头 骨头 骨头深度学习是一种深度学习.发展 发展 发展 发展发展发展发展发展发展.骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折,骨折.骨折风险评估 骨折风险评估 骨折风险评估影像成像技术 影像成像技术机器学习是机器学习.肌肉 肌肉 肌肉 肌肉 肌肉肌肉 肌肉 肌肉 肌肉肌肉骨系统 肌肉骨预测 预测 预测 预测预测 预测 预测 预测预测性 预测性 预测性一个潜在的队列队列.扫描扫描 扫描扫描 扫描扫描断层扫描 (tomography) 是一个非常重要的技术.验证 验证 验证 验证验证验证的时间更多相关视频
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