放射学和人工神经网络建模用于识别高风险的状腺斑块
Chengzhi Gui1, Chen Cao2, Xin Zhang1,3
1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Frontiers in cardiovascular medicine
|July 24, 2023
概括
深度学习模型使用有限的数据准确地分类症状性动脉斑,优于传统的机器学习方法. 这一进步有助于更好地区分斑块类型,以改善患者护理.
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
- 心血管疾病的研究研究.
背景情况:
- Carotid 动脉狭窄带来了严重的中风风险.
- 区分症状斑块 (SP) 和无症状斑块 (ASP) 对于风险分层至关重要.
- 目前用于斑块分类的方法有局限性.
研究的目的:
- 调查和比较两种不同的计算方法来分类症状性动脉斑块.
- 评估基于放射学的机器学习 (ML) 模型与端到端深度学习 (DL) 模型的对比.
- 确定区分有症状和无症状的动脉斑块的高级模型.
主要方法:
- 收集了104名患者 (74名专科医生,30名医生) 的高分辨率磁共振成像 (HRMRI) 数据.
- 开发了两个模型:一个使用基于放射学的ML,另一个使用DL技术 (例如,3D-SE-Densenet-121).
- 使用重复分层五倍交叉验证来评估性能,包括准确性和ROC分析.
主要成果:
- 3D-SE-Densenet-121 DL模型表现出卓越的性能,达到0.9300的AUC和0.9308.8的精度.
- 这种DL模型在精度,精度,灵敏度和F1得分等关键指标上超过了基于放射学的最佳ML模型 (MLP).
- 决策曲线分析表明,3D-SE-Densenet-121模型的临床效用和净益更大.
结论:
- 深度学习模型可以有效地区分症状和无症状的动脉斑块,即使数据有限.
- 在识别症状斑块方面,DL方法显著优于基于放射学的ML模型.
- 这些发现表明DL在改善动脉疾病的分类和管理方面具有很大的前景.
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