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人工智能辅助的缩功能通过心声回声图的解释.
Natsumi Yamaguchi1, Yoshitaka Kosaka1, Akihiko Haga2
1Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.
人工智能 (AI) 改进了心声学中的左心室喷射率 (LVEF) 评估. AI-LVEF减少了变化,并提高了对不那么有经验的读者的准确性,帮助临床决策.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 对于临床决策至关重要的是精确的左心室喷射率 (LVEF) 的回声心脏学评估.
- 人工智能 (AI) 模型在准确估计LVEF方面表现有希望.
研究的目的:
- 为了评估人工智能模型是否可以匹配专家LVEF的读数.
- 确定AI-LVEF是否减少了1级阅读者之间的机构间差异.
主要方法:
- 未来的,多中心的心声回声学研究与五位一级心脏病学家.
- 读者对48个案例进行了两次评估:首先没有AI-LVEF,然后显示AI-LVEF.
- 将LVEF测量与来自五个专家读者的参考平均值进行了比较.
主要成果:
- 在AI-LVEF和参考LVEF之间发现了强烈的相关性 (r=0.90,p<0.001).
- AI-LVEF显著降低了变性 (SD从6.1±2.3减少到2.5±0.9,p<0.001).
- 精度有所提高,平均平方根误差从7.5±3.1降至5.6±3.2 (p=0.004).
结论:
- 人工智能可以帮助1级读者解释心声回应图的缩功能.
- AI-LVEF整合有助于在不同机构之间进行一致和准确的LVEF评估.
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