深度学习用于回声分析,跟踪和评价心脏回 (DELINEATE-MR)
Aaron Long1,2, Christopher M Haggerty2,3, Joshua Finer3
1Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center/New York-Presbyterian Hospital, NY (A.L., G.M., E.D., T.M., M.M., A.J.E., D.K., S.H., Q.L., V.A., M. Lebehn, M. Leon, R.H., P.E., T.J.P.).
Circulation
|June 17, 2024
概括
一个深度学习系统准确地从胸前心声学 (TTE) 研究中分类上 (MR) 的严重程度. 这种人工智能工具表现出高性能, 帮助临床医生改进MR评估.
科学领域:
- 心脏病学
- 人工智能
- 医学成像
背景情况:
- 副心脏吐 (MR) 是一种常见的心脏膜疾病,难以解释.
- 深度学习 (DL) 提供了改善胸腔回声心脏图 (TTE) 分析的潜力.
- 将多个视频评估整合到一个单一的分类是DL在TTE的关键挑战.
研究的目的:
- 从完整的TTE研究中开发和评估一种新的DL系统来对MR严重程度进行分类.
- 根据专家心脏病学家的解释来评估DL系统的性能.
- 为了确定4步和6步MR分类模型的准确性.
主要方法:
- 在超过61,000个TTE上训练了DL系统以识别彩色MR多普勒视频并确定MR的严重程度.
- 在内部和外部试验组中,使用加权卡帕 (κ) 和ROC曲线下的面积 (AUC) 来评估性能.
- 该系统被测试为4步 (无/微量,轻度,中度,严重) 和6步 (包括中间等级) 的MR分类.
主要成果:
- 在内部 (82%准确率, κ=0.84) 和外部 (79%准确率, κ=0.80) 测试组中,DL系统在MR分类中取得了很高的准确性.
- 该模型在中度或较大的MR (AUC=0. 98) 中表现强.
- 六步模型的准确度为80% (内部) 和76% (外部),分别为99%和98%±1步.
结论:
- 一个端到端的DL系统可以从完整的TTE研究中准确地分类MR严重程度.
- 开发的DL系统在协助临床医生进行MR评估方面具有潜在的实用性.
- 该系统能够处理整个心声回声图的研究,为MR评估提供了全面的方法.
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