# Deep Learning for Longitudinal Medical Imaging: A Scoping Review

arXiv: https://arxiv.org/abs/2610.08838 · HTML: https://mlsanity.com/paper/2610.08838/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 96 references that are in the map.
- First posted on arXiv 29 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning](https://mlsanity.com/paper/1910.09420/) (2019)
- [Deep learning for brain metastasis detection and segmentation in longitudinal MRI data](https://mlsanity.com/paper/2112.11833/) (2021)
- [Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks](https://mlsanity.com/paper/1901.01451/) (2019)
- [Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs](https://mlsanity.com/paper/2003.13958/) (2020)
- [Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography](https://mlsanity.com/paper/2209.01676/) (2022)
- [A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study](https://mlsanity.com/paper/2303.10789/) (2023)
- [Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence](https://mlsanity.com/paper/1904.03041/) (2019)
- [Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning](https://mlsanity.com/paper/2203.16606/) (2022)
- [L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction](https://mlsanity.com/paper/2403.16272/) (2024)
- ['A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient](https://mlsanity.com/paper/2210.14228/) (2022)
- [Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor](https://mlsanity.com/paper/2307.06472/) (2023)
- [Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data](https://mlsanity.com/paper/2401.01383/) (2024)

## Abstract (arXiv)

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.