深度学习在纵向医学影像中的应用：范围综述

中文摘要 AI 辅助

纵向医学影像分析是现代医学实践和患者护理的基石。将深度学习应用于纵向影像，通过捕捉随时间变化的空间特征，为增强诊断和追踪疾病进展提供了广阔潜力。随着单时间点深度学习在影像领域的重大进展，鉴于其临床相关性的提高，纵向图像分析引起了越来越多的兴趣，尽管技术挑战依然存在。最近的几项创新可能引领多时间点图像评估的新时代，但科学格局、近期进展和需求领域仍未得到充分描述。为弥补这一空白，我们对应用于纵向医学影像的深度学习方法进行了范围综述，纳入了2018年至2025年间发表的102项研究。神经系统疾病（48%）和眼科疾病（12%）是最常见的临床应用，MRI是主要影像模态（67%）。结合卷积神经网络（CNN）与时间模型（LSTM/RNN）的序列特征建模方法是最常见的方法（40%），其次是跨时间点直接特征聚合（23%）。大多数研究针对分类任务（56%），而仅24%的研究进行了外部验证。我们的研究结果强调，基于深度学习的纵向影像分析仍是一个有前景的领域，尽管更新的时间架构和更大的数据集可能提高这些工具的成功率和临床采用率。

英文摘要

Longitudinal medical imaging analysis is a cornerstone of modern medical practice and patient care. Deep learning applied to longitudinal imaging offers wide potential to enhance diagnosis and track disease progression by capturing spatial changes over time. With major advances in single-timepoint deep learning for imaging, there has been growing interest in longitudinal image analysis, given its increased clinical relevance, though technical challenges remain. Several recent innovations may lead to a new era of multi-timepoint image evaluation, yet the scientific landscape, recent progress, and areas of need remain under-characterized. To address this gap, we conducted a scoping review of deep learning methodologies applied to longitudinal medical imaging, yielding 102 studies published between 2018 and 2025. Neurological disorders (48%) and ophthalmic conditions (12%) were the most common clinical applications, with MRI serving as the predominant imaging modality (67%). Sequential feature modeling approaches combining convolutional neural networks (CNNs) with temporal models (LSTM/RNN) were the most frequent methodology (40%), followed by direct feature aggregation across timepoints (23%). Most studies targeted classification tasks (56%), while external validation was performed in only 24% of studies. Our findings highlight that deep learning-based longitudinal imaging analysis remains a promising field, though newer temporal architectures and larger datasets may improve success and clinical adoption of these tools.

发表机构

• Mass General Brigham（麻省总医院布莱根医疗体系）
• Harvard Medical School（哈佛医学院）
• Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）
• Brigham and Women’s Hospital（布莱根妇女医院）
• University of California, San Francisco（加州大学旧金山分校）
• Inselspital, Bern University Hospital（伯尔尼大学医院（ Inselspital ））
• University of Bern（伯尔尼大学）

机构由 AI 辅助整理，请以论文原文为准。