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一种基于深度学习的方法用于在磁共振图像中诊断布鲁塞拉脊柱炎

Dan Shao, Jinquan Wei, Binyang Wang

IEEE journal of biomedical and health informatics

|April 10, 2025

概括

一个新的深度学习模型,布鲁塞拉脊柱炎MRI诊断网络 (BSMRINet),准确地检测脊柱MRI图像中的布鲁塞拉脊柱炎 (BS). 这种人工智能工具帮助放射科医生诊断这种重要的动物传播疾病.

科学领域:

医疗成像医学成像
人工智能的人工智能
传染性疾病传染性疾病

背景情况:

布鲁塞拉脊柱炎 (BS) 是一种由布鲁塞拉菌引起的广泛的动物性疾病,对全球健康构成挑战.
准确及时诊断BS对于有效的患者管理至关重要.
目前,没有专门的深度学习模型可以在MRI扫描中检测BS.

研究的目的:

开发和验证一个完全自动化的深度学习框架,BSMRINet,用于检测T2加权 (T2W) 的MRI图像中的布鲁塞拉脊柱炎 (BS).
使用人工智能提高BS检测的诊断准确性和效率.

主要方法:

开发了BSMRINet,使用来自四家医院的582名患者队列 (2018年1月 - 2023年8月).
BSMRINet有两个模块:脊椎体病变检测 (ResNet,角落检测) 和脊柱病变检测 (DenseNet,scSE网络).
该模型分析了T2WMRI图像的脊椎病变,磁盘高度,并对脊椎/关节的参与.

主要成果:

在内部和外部验证方面,BSMRINet表现出高度的稳定性和通用性.
与经验丰富的放射科医生相比,该模型在诊断BS的脊柱MRI图像方面取得了卓越的性能.
BSMRINet准确地确定了潜在的布鲁塞拉病变,并评估了相关的脊椎和准脊椎异常.

结论:

BSMRINet为布鲁塞拉脊柱炎的诊断提供了一个有前途的自动化解决方案.
人工智能框架可以显著帮助放射科医生,提高BS的诊断准确性和效率.
这种深度学习方法代表了管理这种普遍的动物传染病的重大进展.

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Magnetic Resonance Imaging

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