Innovative Diagnostic Strategies for Neuroparasitic Diseases in Low-resource Health Systems

Abstract

Neuroparasitic diseases constitute a significant but frequently overlooked component of the global burden of neurological disorders and disproportionately affect populations in low- and middle-income countries. This review evaluates current and emerging diagnostic strategies for major neuroparasitic diseases and considers their operational suitability in resource-constrained settings. Conditions including neurocysticercosis, cerebral malaria, cerebral toxoplasmosis, neuroschistosomiasis, and human African trypanosomiasis contribute substantially to epilepsy, cognitive impairment, neurological disability, and premature mortality. Despite advances in therapeutic interventions, effective disease control remains constrained by persistent diagnostic challenges, particularly in resource-limited settings, where access to neuroimaging, molecular diagnostics, and specialist neurological expertise is severely restricted. Traditional diagnostic approaches rely heavily on neuroimaging modalities supplemented by serological assays and conventional microscopy; these methods are frequently limited by high costs, infrastructure requirements, low sensitivity in early disease, and serological cross-reactivity. Recent technological innovations have generated renewed optimism regarding the diagnosis of neuroparasitic infections in underserved regions. Antigen-detection assays capable of identifying active infections, cerebrospinal fluid polymerase chain reaction, and artificial intelligence-assisted microscopy have emerged as promising alternatives. Advances in portable molecular technologies, digital pathology, smartphone-based imaging systems, and machine-learning algorithms further suggest the possibility of bringing highly sensitive diagnostic capabilities closer to the point of care. Applying the World Health Organization ASSURED framework as an evaluative lens, this review contends that future progress depends less on incremental gains in analytical sensitivity and more on the structural reorganisation of diagnostic networks. Sustained investment in laboratory workforce development and the integration of digital health infrastructure are required to connect frontline facilities with specialist expertise. Only through systemic alignment can innovative diagnostics for neuroparasitic diseases effectively narrow the gap between scientific capability and population health needs.