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.
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- Green open access
Cite this article
APA 7
Mussa, F., Tak, D., Avval, A. H., Brueningk, S., Mak, R. H., Aerts, H. J. W. L., Rauschecker, A. M., & Kann, B. H. (2026). Deep Learning for Longitudinal Medical Imaging: A Scoping Review. https://omanscience.com/en/articles/deep-learning-for-longitudinal-medical-imaging-a-scoping-review
MLA 9
Mussa, Francesca, et al. "Deep Learning for Longitudinal Medical Imaging: A Scoping Review." https://omanscience.com/en/articles/deep-learning-for-longitudinal-medical-imaging-a-scoping-review.
Chicago (author–date)
Mussa, Francesca, Divyanshu Tak, Atlas H. Avval, Sarah Brueningk, Ray H. Mak, Hugo J. W. L. Aerts, Andreas M Rauschecker, and Benjamin H. Kann. 2026. "Deep Learning for Longitudinal Medical Imaging: A Scoping Review." https://omanscience.com/en/articles/deep-learning-for-longitudinal-medical-imaging-a-scoping-review.
Harvard
Mussa, F., Tak, D., Avval, A. H., Brueningk, S., Mak, R. H., Aerts, H. J. W. L., Rauschecker, A. M. and Kann, B. H. (2026) 'Deep Learning for Longitudinal Medical Imaging: A Scoping Review', Available at: https://omanscience.com/en/articles/deep-learning-for-longitudinal-medical-imaging-a-scoping-review.
Vancouver
Mussa F, Tak D, Avval AH, Brueningk S, Mak RH, Aerts HJWL, et al. Deep Learning for Longitudinal Medical Imaging: A Scoping Review. https://omanscience.com/en/articles/deep-learning-for-longitudinal-medical-imaging-a-scoping-review
IEEE
F. Mussa, D. Tak, A. H. Avval, S. Brueningk, R. H. Mak, H. J. W. L. Aerts, A. M. Rauschecker, and B. H. Kann, "Deep Learning for Longitudinal Medical Imaging: A Scoping Review," https://omanscience.com/en/articles/deep-learning-for-longitudinal-medical-imaging-a-scoping-review.