🤖 AI Summary
Longitudinal medical image analysis lacks a systematic review, leaving its technical challenges and scientific landscape unclear. This study addresses this gap by conducting a scoping review of 102 deep learning-related studies published between 2018 and 2025, systematically synthesizing their methodological architectures, clinical applications, and validation strategies. The findings reveal that CNN-LSTM serves as the predominant architecture, MRI as the primary modality, and classification tasks within neurology and ophthalmology as the dominant applications. This work provides the first comprehensive delineation of the technical landscape in this field while identifying the critical bottleneck of severely insufficient external validation. Ultimately, it offers clear directions for advancing the clinical translation of artificial intelligence models applied to longitudinal medical imaging.
📝 Abstract
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.