开发半自动算法,用于后侧静脉曲的心声诊断后侧静脉曲
Nicolò Bergo1, Alessandro Piccolo2, Francesca Maria Susin3
1Department of Surgical, Medical & Molecular Pathology & Critical Care Medicine, University of Pisa, Pisa, Italy; Department of Civil, Environmental & Architectural Engineering, University of Padova, Padova, Italy; The BioRobotics Institute & Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
新的心声回声学方法 - - 中心环到下基底壁旋转角度 (MIRA) 和中心环到下基底壁角度 (MAIBA) - - 在诊断后侧缩 (PSC) 方面显示出高准确度. 这些工具可以更好地识别患有心室节律失常和心脏突然死亡风险的患者.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗成像医学成像
背景情况:
- 后侧收缩曲线 (PSC) 是恶性心室节律失常和心脏突然死亡的已知的危险因素.
- 准确诊断PSC对于风险分层和患者管理至关重要.
研究的目的:
- 开发和验证用于量化PSC的半自动算法.
- 引入新的心声回声学参数:中枢环向低基底壁旋转角 (MIRA) 和中枢环向低基底壁旋转角 (MAIBA).
主要方法:
- 使用心脏组织跟踪和图像处理技术.
- 算法在100名患者身上进行了测试,这些患者使用横侧心脏回声扫描在侧长轴视图中进行测试.
- 患者被分为PSC阳性和PSC阴性组.
主要成果:
- MIRA和MAIBA与手动测量有很强的相关性 (皮尔森的r=0.70和0.80).
- 鉴定PSC的诊断准确率为MIRA的76%和MAIBA的85%.
- 通过布兰德-阿尔特曼分析显示出卓越的观察者间可靠性 (ICC>0.97) 和一致性.
结论:
- MIRA和MAIBA角度是可靠和准确的PSC诊断的有希望的工具.
- 这些参数可以增强临床决策,识别心律失常风险.
- 该研究支持在管理PSC时改善诊断支持的潜力.
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