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在MRI上进行腰椎脊柱狭窄的深度学习模型:模型比较和临床基准测试
You Jun Lee1, Changshuo Liu2, Yong Han Ting1,3
1Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
概括
深度学习模型在MRI上准确地分类腰椎脊柱狭窄,CNN的表现优于其他模型,并且与临床医生的表现相匹配. 这些人工智能工具可以帮助进行手术规划和医学教育.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 脊柱外科手术 脊柱外科手术
背景情况:
- 腰椎脊柱狭窄的诊断依赖于成像解释.
- 使用深度学习进行自动分类,有可能提高效率和准确性.
研究的目的:
- 为了比较深度学习模型 (基于CNN和变压器) 在MRI上进行自动腰椎脊柱狭窄症分类.
- 为了将他们的表现与放射科医生和骨科医生进行比较.
主要方法:
- 对564项腰椎脊柱MRI研究的回顾性分析.
- 开发CNN和变压器模型用于狭窄症分类.
- 对8名临床医生的绩效评估使用检测回忆,灵敏度,特异性和评价者间协议 (Gwet κ).
主要成果:
- 两种深度学习模型在所有感兴趣的区域都实现了高回忆率 (>94%).
- 在CNN和变压器模型中,对狭窄症分类的专家共识有很高的一致性 (κ=0.90-0.99),与临床医生相比或优于临床医生.
- 在内部和外部测试集中,性能一致.
结论:
- 深度学习模型,特别是CNN,在MRI的腰椎脊柱狭窄症分类方面,表现与临床医生相比或优于临床医生.
- 这些人工智能工具有可能协助临床报告生成,手术规划和医学教育.
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