A scoping review of 102 studies published between 2018 and 2025 reveals that deep learning is a promising field for longitudinal medical imaging analysis. The review found that neurological disorders were the most common clinical application, followed by ophthalmic conditions, with MRI being the predominant imaging modality. While sequential feature modeling approaches combining CNNs with temporal models like LSTM/RNN were frequently used, the study highlights a need for newer temporal architectures and larger datasets to improve clinical adoption. AI
IMPACT Deep learning in longitudinal medical imaging shows promise for improved diagnosis and disease tracking, though further development in temporal architectures and data is needed for wider clinical adoption.
RANK_REASON The item is a research paper detailing a scoping review of deep learning methodologies applied to longitudinal medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- convolutional neural network
- deep learning
- Francesca Romana Mussa Ms
- Longitudinal medical imaging
- long short-term memory
- magnetic resonance imaging
- recurrent neural network
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