通过深度学习算法对体重变化患者的超声波图像进行肥胖症量化
Adam P Harrison1, Bowen Li2, Tse-Hwa Hsu3
1Research Division, Riverain Technologies, Miamisburg, OH 45342, USA.
Diagnostics (Basel, Switzerland)
|October 28, 2023
概括
一个深度学习算法准确地从超声波图像中量化肝脏肥胖症,与体重变化和性别相关. 正确的肋间视图是客观肥胖症评估的最佳选择.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肝病学 肝病学是一种肝病学.
背景情况:
- 肝硬化症的诊断往往是主观的.
- 需要在超声波图像上使用深度学习进行客观量化.
- 这项研究评估了重量波动患者的深度学习算法.
研究的目的:
- 评估深度学习算法的准确性,以量化肝脏肥胖症.
- 为了将算法衍生的肥胖症得分与患者体重变化相关联.
- 为了确定算法的最佳超声波扫描视图.
主要方法:
- 74名患者的超声波研究的回顾性分析,体重变化≥5%.
- 应用一个深度学习算法来分类四个扫描视图的脂肪病.
- 肥胖症得分与体重变化和人口统计数据的相关性.
主要成果:
- 深度学习算法在不同视图中显示出高一致性 (R2 > 0.86).
- 肥胖症得分与体重变化有显著的相关性 (R2 = 0.62,p < 0.001).
- 男人表现出比女性更高的肝硬化/BMI比率 (p = 0.026).
结论:
- 建议使用 3-5 张图像进行右侧肋间视图,以获得最佳的肥胖症量化.
- 深度学习算法提供客观的肝硬化症评估.
- 量化肥胖症与体重波动和性别有关.
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