Milosevic Marko, Jin Qingchu, Singh Akarsh, Amal Saeed
Roux Institute, Northeastern University, Portland, ME, United States.
College of Engineering, Northeastern University, Boston, MA, United States.
Front Radiol. 2024 Jan 12;3:1294068. doi: 10.3389/fradi.2023.1294068. eCollection 2023.
Data for healthcare is diverse and includes many different modalities. Traditional approaches to Artificial Intelligence for cardiovascular disease were typically limited to single modalities. With the proliferation of diverse datasets and new methods in AI, we are now able to integrate different modalities, such as magnetic resonance scans, computerized tomography scans, echocardiography, x-rays, and electronic health records. In this paper, we review research from the last 5 years in applications of AI to multi-modal imaging. There have been many promising results in registration, segmentation, and fusion of different magnetic resonance imaging modalities with each other and computer tomography scans, but there are still many challenges that need to be addressed. Only a few papers have addressed modalities such as x-ray, echocardiography, or non-imaging modalities. As for prediction or classification tasks, there have only been a couple of papers that use multiple modalities in the cardiovascular domain. Furthermore, no models have been implemented or tested in real world cardiovascular clinical settings.
医疗保健数据多种多样，包括许多不同的形式。传统的用于心血管疾病的人工智能方法通常局限于单一形式。随着多样化数据集和人工智能新方法的激增，我们现在能够整合不同的形式，如磁共振扫描、计算机断层扫描、超声心动图、x光和电子健康记录。在本文中，我们回顾了过去5年人工智能在多模态成像应用方面的研究。在不同磁共振成像模态之间以及与计算机断层扫描的配准、分割和融合方面已经取得了许多有前景的成果，但仍有许多挑战需要解决。只有少数论文涉及x光、超声心动图或非成像模态等形式。至于预测或分类任务，在心血管领域只有几篇论文使用了多种模态。此外，还没有模型在现实世界的心血管临床环境中得到实施或测试。
