开发和临床验证深度学习用于自诊断上关节裂的深度学习
Deming Guo1,2, Xiaoning Liu1, Dawei Wang3
1Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China.
Journal of orthopaedic surgery and research
|June 12, 2023
概括
一个深度学习模型从肩膀MRI扫描中准确诊断上关节撕裂 (STs),匹配初级临床医生的表现. 这种人工智能工具可以帮助经验较少的放射科医生有效地诊断ST.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 通过MRI诊断超脊柱裂 (STs) 是具有挑战性的,因为放射科医生经验的变化.
- 在肩膀MRI上使用深度学习对ST的自动诊断是一种潜在的解决方案.
- 这项研究验证了在临床实践中用于ST诊断的深度学习模型.
研究的目的:
- 开发和验证一种基于深度学习的模型,用于使用肩部MRI进行自动化超脊椎撕裂诊断.
- 评估开发模型的诊断性能和临床可行性.
主要方法:
- 701个肩部MRI数据集 (2804张图像) 的回顾性收集,用于培训和内部测试.
- 一个单独的69个肩部MRI (276张图像) 组,来自接受肩部关节整形手术的患者,用于临床验证.
- 训练基于Xception架构的两个卷积神经网络 (CNN),用于ST检测和评估性能指标 (灵敏度,特异性,精度,准确性,F1得分).
主要成果:
- 2D CNN模型实现了最佳的诊断性能,F1分数为0.824 (手术组) 和0.75 (内部组).
- 在手术和内部测试组中,ROC AUC分别为0.921和0.882.
- 该模型的诊断性能与高级临床医生相比,优于初级临床医生,1.5T和3.0TMRI数据之间没有显著差异.
结论:
- 开发的2D CNN模型提供了足够和高效的自动诊断上关节撕裂.
- 该模型的性能与初级肌肉骨放射科医生和整形外科医生的性能相当.
- 这种人工智能工具可以帮助经验较少的放射科医生,特别是在缺乏专家咨询的环境中.
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