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一个混合细分和分类CAD框架,用于自动预测MRI图像的心肌梗塞
Mugahed A Al-Antari1, Riyadh M Al-Tam2, Aymen M Al-Hejri2
1Department of Artificial Intelligence and Data Science, College of AI Convergence, Daeyang AI Center, Sejong University, Seoul, 05006, Korea. en.mualshz@sejong.ac.kr.
Scientific reports
|April 24, 2025
概括
这项研究引入了使用MRI扫描进行心肌梗塞 (MI) 的计算机辅助诊断 (CAD) 系统. 这种新的并行方法准确地细分和分类MI,提高了诊断能力.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 早期诊断心肌梗塞 (MI) 对于心脏功能保护和患者的治疗结果至关重要.
- 在MRI中精确的心脏区域细分和分类对于有效的MI评估至关重要.
研究的目的:
- 开发和评估一种创新的计算机辅助诊断 (CAD) 系统,用于使用MRI图像同时对MI进行细分和分类.
- 为了比较使用MRI进行MI诊断的串行与并行方法的性能.
- 评估基于人工智能的不同分类策略的有效性,包括混合CNN-视觉变压器 (ViT) 模型.
主要方法:
- 开发了一种多类细分模型 (ResU-Net) 来识别左心室腔 (LV),正常的心肌 (Myo),心肌梗塞和微血管阻塞 (MVO).
- 采用了三种基于人工智能的分类策略:单个深度学习模型,多个人工智能模型的功能融合,以及混合CNN-ViT组合模型.
- 该系统在EMIDECMRI数据集上使用串行和并行方法进行了评估,并进行了五次交叉验证.
主要成果:
- 该ResU-Net细分模型获得了高的F1分数:91.12% (LV),88.39% (Myo),80.08% (MI) 和68.01% (MVO).
- 与混合CNN-ViT模型的并行方法显示出优异的分类性能,达到98.15%的准确性和98.63%的F1得分.
- 拟议的CAD系统显示出在MI诊断中临床应用的巨大潜力.
结论:
- 开发的CAD系统,特别是利用混合CNN-ViT模型的并行方法,为MRI的MI细分和分类提供了强大而准确的方法.
- 该系统可以成为医疗保健专业人员的宝贵工具,有助于精确的MI诊断,治疗计划和患者护理.
- 这些发现强调了人工智能在增强心血管成像分析方面的潜力,以改善临床决策.
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