通过对成像和临床数据的深度学习整合,提高了高血压患者中风风险预测
Hui Li1, Tianyu Zhang2, Guochao Han1
1Neuroelectrophysiology Department, The Second Affiliated Hospital of Qiqihar Medical College, No. 37, Zhonghua West Road, Jianhua District, Qiqihar, Heilongjiang Province, 161000, China.
BMC medical informatics and decision making
|August 1, 2025
概括
这项研究开发了一种深度学习模型,整合了带超声波图像和临床数据,以预测高血压患者中风风险,从而实现早期检测和干预的高准确性.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 脑卒中预防研究 研究 研究
背景情况:
- 在全球范围内,中风是导致死亡和残疾的主要原因,高血压是主要的危险因素.
- 目前的中风风险评估方法往往缺乏准确性,因为临床参数有限,并排除成像特征.
研究的目的:
- 开发一种深度学习多式模式,用于精确预测高血压患者中风风险.
- 整合带超声波成像与临床数据,以提高风险分层.
主要方法:
- 利用了1,088名高血压患者的2,176张动脉超声图像.
- 使用ResNet50进行图像细分和特征提取,通过视觉转换器 (ViT) 与临床数据融合.
- 风险分层使用辐射基概率神经网络 (RBPNN) 进行,通过AUC,Dice,IoU和Precision-Recall曲线评估性能.
主要成果:
- 多式联络融合模型实现了AUC为0.97,子系数为0.90,IOU为0.80.
- 废弃性研究证实ViT和RBPNN模块显著提高了性能.
- 该模型在高风险小组中表现强,包括糖尿病患者和吸烟患者.
结论:
- 深度学习多式模式准确地整合成像和临床数据,以改善高血压患者中风风险预测.
- 该模型显示出强大的通用性和临床应用潜力,用于早期中风查和个性化预防策略.
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