Integrating artificial intelligence with nanodiagnostics for early detection and precision management of neurodegenerative diseases

Abstract

Background: Neurodegenerative diseases—including Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis (ALS)—as well as autoimmune disorders with neurodegenerative features such as multiple sclerosis (MS), present an escalating global challenge. Current diagnostics often detect pathology too late, and most treatments focus on symptom relief rather than disease modification. There is an urgent need for tools that enable early detection and precision-targeted intervention.

Main body: Nanotechnology offers unique advantages in this space, enabling early molecular detection, targeted drug delivery, and theranostic systems. Engineered nanocarriers, biosensors, and responsive nanodevices are being tailored to disease-specific features such as oxidative stress in AD or neuroinflammation in MS. Yet, issues like biocompatibility, clinical scalability, and long-term safety remain barriers to translation. Artificial intelligence (AI) enhances nanomedicine by improving biomarker sensitivity, stratifying patients, and enabling predictive disease modeling. From AI-guided nanoparticle design to closed-loop delivery systems and digital twin models, these technologies work synergistically to support real-time, personalized care. Still, critical challenges—including algorithmic bias, lack of explainability, heterogeneous datasets, and limited regulatory clarity—impede clinical integration. Additionally, high system complexity and cost risk excluding low-resource settings unless inclusive, scalable alternatives are pursued.

Conclusion: The convergence of AI and nanotechnology is reshaping neurodegenerative disease care, moving from reactive to proactive, personalized neurology. Realizing this promise requires cross-sector collaboration, ethical foresight, and translational rigor to ensure these innovations are safe, equitable, and accessible to all patients.

Graphical Abstract:

Keywords: Biomarker detection; Blood–brain barrier; Early diagnosis; Nanomedicine; Targeted drug delivery; Theranostics.

Fig. 1

Schematic representation of pathophysiological mechanisms in major neurodegenerative diseases. (A) Alzheimer’s disease (AD): Abnormal cleavage of amyloid precursor protein (APP) produces amyloid-β (Aβ) peptides that aggregate into extracellular plaques. Concurrently, hyperphosphorylated tau accumulates intracellularly to form neurofibrillary tangles. These processes contribute to synaptic dysfunction, hippocampal atrophy, and progressive neurodegeneration. (B) Parkinson’s disease (PD): Misfolding and aggregation of α-synuclein into Lewy bodies occurs within dopaminergic neurons of the substantia nigra. This disrupts mitochondrial function and proteostasis, ultimately causing dopaminergic neuronal loss and motor impairmen

Fig. 2

Schematic representation of nanotechnology-enabled strategies for detection and imaging of neurodegenerative disease biomarkers. (A) Ex vivo biomarker capture: Tau protein and β-amyloid peptides are selectively recognized by antibodies (Y-shaped symbols) and aptamers (loop structures) from blood and cerebrospinal fluid (CSF) samples. (B) Advanced optical detection: Surface-enhanced Raman spectroscopy (SERS), fluorescence spectroscopy, and localized surface plasmon resonance (LSPR) provide sensitive and specific readouts of the captured biomarkers. (C) Spectral analysis: Representative fluorescence intensity profiles are shown for β-amyloid signals, illustrating the distinct optical signatures that enable biomarker discrimination. (D) In vivo imaging applications: Nanoparticle-assisted MRI and CT imaging improve visualization of β-amyloid plaque deposition in the brain, offering enhanced contrast and diagnostic accuracy. Note: MRI/CT images are schematic illustrations

Fig. 3

Schematic representation of a dopamine/phthalocyanine-loaded nanosystem for blood–brain barrier transport. (A) Nanoparticle structure: Poly(lactic-co-glycolic acid) (PLGA) nanoparticle co-loaded with aluminum chloride phthalocyanine (AlClPc) and dopamine. The schematic illustrates the nanoparticle core and encapsulated therapeutic agents. (B) Mechanism of blood–brain barrier (BBB) penetration: The nanosystem traverses brain endothelial cells and subsequently releases dopamine and AlClPc into the brain parenchyma, where they can exert therapeutic effects on neuronal tissue

Fig. 4

Schematic integration of artificial intelligence (AI) with neuroimaging for monitoring neurodegeneration. (A) Pathological features: Key imaging markers include hippocampal atrophy, white matter disruption, and amyloid-β plaque accumulation. (B) AI pipeline: Multi-step workflow involving preprocessing, brain region segmentation (convolutional neural networks), feature extraction (e.g., cortical thickness, PET standardized uptake value ratio [SUVR] maps, diffusion tensor imaging [DTI] fractional anisotropy), and disease risk prediction using deep learning models (e.g., ResNet, XGBoost). (C) Clinical outputs: Schematic examples include heatmaps of cortical atrophy, tractography of white matter pathways, and predictive dashboards integrating imaging features with cognitive scores and cerebrospinal fluid (CSF) biomarkers

Fig. 5

Schematic illustration of AI-integrated smart nanodevices and closed-loop neurotherapeutic systems. A) The left panel shows implantable nanoelectronic sensors designed for real-time neurochemical monitoring. These devices detect biomarkers such as land reactive oxygen species (ROS) and transmit the data to AI systems for analysis. Upon identification of pathological signals, responsive nanocarriers release therapeutic agents in a targeted manner, providing feedback-driven treatment with minimal off-target effects. B) The right panel depicts a closed-loop therapeutic platform that integrates nanoparticle-based drug delivery, AI-mediated signal processing, and electroencephalogram (EEG) signal acquisition. This adaptive system adjusts therapeutic interventions in real time based on neurophysiological feedback, representing a potential pathway toward personalized, self-regulating neurotherapeutics

Fig. 6

Schematic representation of digital twin modeling for neurodegenerative disease progression. A patient-specific digital twin of the brain is constructed by integrating biomarker profiles, nanosensor readouts, and clinical imaging data through artificial intelligence (AI) models. The virtual model simulates key pathological processes—including protein aggregation, synaptic dysfunction, and neuronal atrophy—in a temporally coherent manner. This platform enables in silico testing of therapeutic strategies, such as nanoparticle-based drug delivery, to optimize treatment timing and dosage, ultimately supporting personalized intervention strategies