Deep Learning-Based Multimodal Neuroimaging Analysis for Early Detection of Neurodegenerative Diseases Using Brain CT and Clinical Data
Keywords:
multimodal neuroimaging; deep learning; brain CT; clinical data; neurodegenerative diseases; early detection; clinical decision support; fairness; governance; health infrastructureAbstract
Early detection of neurodegenerative diseases remains a difficult clinical and infrastructural challenge because the earliest biological changes often precede obvious symptoms and because access to advanced imaging is unevenly distributed. This paper examines deep learning-based multimodal neuroimaging analysis for early detection using brain computed tomography and clinical data as a system-level intervention. Although magnetic resonance imaging and positron emission tomography provide richer tissue contrast and molecular specificity, computed tomography is widely available, fast, and central to acute care pathways, making it a pragmatic anchor for scalable screening and triage. The proposed analytical perspective treats deep learning as one component within a socio-technical architecture that includes data acquisition, preprocessing, multimodal fusion, clinical decision support, governance, deployment, and long-term monitoring. The paper discusses architectural trade-offs among centralized cloud inference, edge deployment, and hybrid systems, with attention to latency, privacy, bandwidth, maintenance, and resilience. It also analyzes fairness and robustness risks arising from site-specific imaging protocols, demographic skew, missing clinical variables, and distributional drift across hospitals. Governance mechanisms such as prospective validation, human oversight, auditability, post-market surveillance, and transparent reporting are presented as necessary complements to model performance. The conclusion argues that successful early detection systems must be designed not merely as high-performing classifiers but as sustainable clinical infrastructures that align technical capability with reimbursement, workforce capacity, regulatory pathways, and equitable access. Such systems can improve triage and referral only if they are embedded responsibly within existing neurological and primary care workflows.
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