机器学习vs人类专家:从RAPID-axSpA和C-OPTIMISE第三阶段axSpA试验中进行的圣炎分析
Fabian Proft1, Janis L Vahldiek2, Joeri Nicolaes3,4
1Department of Gastroenterology, Infectious Diseases and Rheumatology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Rheumatology advances in practice
|April 21, 2025
概括
一种新的深度学习模型可以准确地检测轴性脊柱关节炎 (axSpA) 患者的放射性神经炎,从而有可能加快诊断和改善护理. 这种人工智能工具在减少 axSpA. axSpA. 的X射线解释变异性方面表现出有希望.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 风湿病学和免疫学
背景情况:
- 轴性脊椎关节炎 (axSpA) 的诊断依赖于识别放射性神经炎.
- 传统的X光学解释存在显著的interreader变化,影响诊断的准确性和及时性.
- 机器学习为标准化和加快axSpA诊断提供了一个潜在的解决方案.
研究的目的:
- 评估深度学习 (DL) 模型在 axSpA.患者中检测放射性神经炎的性能.
- 评估DL模型的准确性与不同临床试验队伍的专家读者.
- 确定DL在减少诊断变异性和改善axSpA患者护理途径方面的潜力.
主要方法:
- 从RAPID-axSpA和C-OPTIMISE临床试验中进行的X线图的追溯分析.
- 使用DL模型,先前使用转移学习方法对非医疗数据进行训练.
- 模型性能通过将其读数与中央专家读数进行比较来评估,计算灵敏度,特异性和科恩的kappa.
主要成果:
- DL模型在RAPID-axSpA队列中表现出强的表现 (82%的灵敏度,81%的特异性,科恩的 κ=0.61) 和在C-OPTIMISE队列中表现出良好的表现 (90%的灵敏度,56%的特异性,科恩的 κ=0.48).
- 与中央阅读器的模型协议为RAPID-axSpA的82%,C-OPTIMISE的75%.
- 结果表明,DL模型与专家读者在检测放射性圣炎的性能密切匹配.
结论:
- 评估的深度学习模型在不同的临床试验环境中准确地检测axSpA患者的放射性神经炎.
- 这种人工智能工具有可能加快axSpA的诊断,减少医疗保健资源的利用,并提高患者管理.
- 这些发现支持将DL模型集成到临床工作流程中,以改善axSpA诊断和患者护理.
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