Mamba-Driven Longitudinal Modeling for Disease Progression Prediction from Medical Imaging
Keywords:
longitudinal medical imaging, disease progression prediction, selective state space models, Mamba, clinical AI infrastructure, model governance, fairness, federated learningAbstract
Longitudinal medical imaging contains high-dimensional observations of anatomical and pathological change over time, yet most deep learning systems in medical image analysis remain oriented toward cross-sectional classification or segmentation. Disease progression prediction requires a temporal modeling perspective that can accommodate irregular follow-up schedules, missing examinations, heterogeneous acquisition protocols, and clinically meaningful intervals between scans. This paper analyzes Mamba-driven longitudinal modeling as a systems-oriented approach to progression prediction from medical imaging. Selective state space models offer linear-time sequence processing and content-dependent memory, making them structurally attractive for long imaging trajectories. However, the central argument of this paper is that architectural efficiency alone does not determine clinical value. The effective deployment of Mamba-driven longitudinal models depends on the broader infrastructure for cohort governance, temporal data partitioning, harmonization, privacy-preserving learning, robustness monitoring, calibration, fairness auditing, and sustainable lifecycle management. The paper develops a layered system architecture that separates image harmonization, spatial encoding, temporal state-space reasoning, and clinical prediction, while analyzing the structural trade-offs that arise at each boundary. It further examines federated learning for distributed longitudinal cohorts, temporal leakage prevention, domain shift, model documentation, fairness feedback loops, and energy consumption. The discussion integrates perspectives from medical image analysis, sequence modeling, clinical informatics, and socio-technical systems research. The paper concludes that Mamba-driven longitudinal models should be treated not as standalone algorithms but as components of governed, continuously evaluated clinical learning systems. Future progress will require stronger cohort resources, longitudinal evaluation standards, and institutional accountability mechanisms that can sustain these models across their full lifecycle.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
