心血管成像中的深度学习的挑战和策略:喷射分数和心力衰竭管理
David Pasdeloup1, Andreas Østvik2, Sindre Olaisen1
1Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.
JACC. Cardiovascular imaging
|July 9, 2025
概括
用于心脏测量的深度学习 (DL) 在评估指标,训练数据和概括方面面临挑战. 结合成像领域的知识,提高了DL模型在临床使用中的稳定性.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 深度学习 (DL) 对自动心脏测量非常有希望.
- 临床翻译DL方法面临的实施挑战.
研究的目的:
- 评估基于DL的心脏测量使用左心室喷射分数 (LVEF) 进行心力衰竭 (HF) 管理的挑战.
- 讨论针对已确定的DL挑战的缓解策略.
主要方法:
- 利用监督端到端学习用于在三个不同的群体 (N=3,538) 中进行自动化LVEF测量.
- 研究了与评估指标,培训数据和模型概括相关的挑战.
- 在高频管理的背景下分析了DL模型的性能.
主要成果:
- 由于人口变异,AUC等评估指标显示二分化HF诊断的不可靠性.
- 即使训练数据减少了40%,模型性能也得到了改善.
- 观察到泛化挑战与外部数据的性能退化;域知识整合提高了性能.
结论:
- 训练数据特征和模型概括对心脏成像中的DL构成重大挑战.
- 评估指标可以掩盖表现不佳的DL算法.
- 整合成像领域的知识和仔细考虑数据,提高DL模型设计,评估和临床适用性.
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