The nano-neuro nexus: smart sensors at the frontier of detecting neurodegeneration

1 Introduction

Neurodegenerative disorders (NDDs), such as, Alzheimer’s disease (AD), Parkinson’s disease (PD) and Amyotrophic Lateral Sclerosis (ALS) are progressively recognized as one of the most alarming and rapidly expanding challenges in global health, due to aging populations and absence of effective therapeutic approaches (Sood and Mehrotra, 2026; Wilson et al., 2023). Despite decades of extensive investigations, clinical management of such conditions remains largely elusive, since diagnostic interpretation is typically undertaken only after significant and irreversible neuronal loss has already been reported (Dhariwal et al., 2025). The substantial delay in diagnosis of NDDs is not just a clinical practice constraint, rather a profound biological and technological shortcoming of current diagnostic tools, presently possessing the inability to sensitively and non-invasively detect early molecular perturbations, which often begins years or even decades before the onset of characteristic clinical symptoms (Erkkinen et al., 2018), thereby offering an extended pre-clinical window that remains largely unapproachable to current diagnostic modalities.

Traditional approaches to diagnose NDDs have shifted the paradigm towards early, pathology-based identification; nevertheless they encounter substantial practical and biological constraints which limit their widespread application. While positron emission tomography (PET) and magnetic resonance imaging (MRI), as well as cerebrospinal fluid (CSF) biomarker analysis offers greater sensitivity in identifying proteinopathies (e.g., amyloid-β, tau) and their associated biophysical structural alterations, but they are often expensive, invasive and restricted to specialized diagnostic centers (Song et al., 2025). Moreover, such neuroimaging methods are often expensive, requires specialized infrastructure and frequently lacks satisfactory sensitivity to efficiently detect early-stage molecular changes, whereas CSF biomarker analysis, although clinically informative, is invasive and unsuitable for routine or large-scale screening purposes (Bhatia et al., 2025). Recently, blood-based biomarkers, while extremely promising for NDDs diagnosis have shown significant analytical constrains principally due to un-detectable or ultra-low concentration of brain-derived proteins in the bloodstream (often present in the femtomolar to picomolar range), including subsequent presence of masking effects offered by high-abundance plasma proteins (Alcolea et al., 2023). Therefore, such limitations demand a critical requirement for developing diagnostic platforms proficient enough of detecting ultra-low concentrations of disease-relevant biomarkers with high specificity, reproducibility and scalability.

Recently, nanotechnological advancements have emerged as a transformative force in neurodegeneration diagnostics and fosters to bridge the gap between understated early-stage pathologies detection and has significantly overcome the limitations posed by traditional, invasive or low-sensitivity diagnostic tools (Yadav et al., 2025; Zhang et al., 2018). The key focus of these findings was on platform development, biomarker detection and translational challenges. Whereas, the present review is more inclined towards nano-neuro nexus framework, which is a multifaceted integration of neurobiological signals, it is sensing at nanoscale and subsequent computational-based data interpretation. This approach extends beyond conventional technology-centric viewpoints by highlighting dynamic connexion across these domains. Additionally, the review focuses on the concept of silent signals, emphasizing on temporally early and low-abundance biomarkers, thereby integrates emerging artificial intelligence-driven diagnostic approaches. By incorporating the most recent literature findings, the manuscript offers a systems-level, progressive approach that complements and advances previously published reports as well. By operating at the nanoscale (1–100 nm) level, wherein biological interactions occur at the level of molecules and macromolecular complexes, nanodiagnostic systems have been documented to directly interact with the fundamental molecular drivers of NDDs pathology by mimicking as biological structures or via precisely acting on proteinaceous misfolding sites at the molecular level (Patra et al., 2018). The distinctive physicochemical properties of nanomaterials, including their remarkably high surface area-to-volume ratios, adjustable optical and electronic characteristics and the modifiable capacity for their facile surface functionalization, empowers them to be developed as highly sensitive and selective biosensors with in-built proficiency of detecting specific biomarkers at nanoscale concentrations (Darwish et al., 2024; Sandhir et al., 2014). These biosensors are predominantly well-suited for the detection of early so called “silent signals” of neurodegeneration, such as circulating misfolded protein oligomers, oxidative stress markers and extracellular vesicle-associated cargo, which are often present at exceptionally low levels during the pre-clinical phase (Khatami et al., 2025). The merging of nanotechnology with neuroscience, herein hypothesized as the “nano-neuro nexus,” signifies a paradigm shift in the approach to NDDs diagnostics. This interdisciplinary framework assimilates advances in materials science, bioengineering, molecular biology and computational-based analytical approaches to concomitantly support real-time, marginally invasive and multiplexed detection of disease specific associated biomarkers (Davis Jr et al., 2025). Notably, the amalgamation of potential nanodiagnostic platforms with emerging technologies such as microfluidics and artificial intelligence additionally boosts their analytical capacity, thereby assisting in high-throughput data acquisition, specific pattern recognition and predictive modeling (Pathak et al., 2025). Such advance systems are documented to offer the potential not only to detect specific disease pathology early, but also aids to monitor disease progression dynamically, including subsequent assessment of therapeutic responses in a personalized manner.

However, successful translation of nanodiagnostic inventions from laboratory to their clinical applications is a complicated process mired by technical, regulatory and economic challenges. Primarily, the existence of their ethical concerns related to such systems biocompatibility issues, reproducibility obligations, mandatory regulatory approval and cost-effectiveness must be meticulously addressed to assess their actual clinical potential (Agrahari and Hiremath, 2017; Wasti et al., 2023). Furthermore, the complexity of NDDs, characterized by heterogeneity in pathological progression and their simultaneous clinical presentation, demands the pharmacological development of integrative diagnostic methodologies that could hold the potential to analyze multiple biomarker properties simultaneously (Zhu et al., 2025). Therefore, the present manuscript aims to summarize a comprehensive and critical synthesis of existing advancements in nanodiagnostics for NDDs detection, with specific emphasis on recognizing early molecular signals, development of smart nanosensor systems, including the challenges and new avenues associated with clinical translation. By identifying the complex interplay between applications of nanoscale technologies with diagnosis of specific neural pathology, this manuscript pursues to highlight the transformative potential of nanodiagnostics in redefining the landscape of NDDs management, shifting the criteria from late-stage diagnosis to early, accurate and proactive intervention.

In the present manuscript, the nano-neuro nexus is operationally defined as a multifaceted diagnostic framework that functionally incorporates: (i). Neurobiological signal generation, (ii). Nanoscale sensing and transduction, and (iii). Computational interpretation into a cohesive system for early disease detection. Discrete from the broader conception of applying nanotechnology to neurodegeneration, the nano-neuro nexus highlights dynamic coupling across scales, linking molecular pathology (such as, misfolded proteins, extracellular vesicles and redox markers) with engineered nano-interfaces capable of selective capture, amplification and signal conversion, with concomitant application of algorithmic pipelines that efficiently decode high-dimensional outputs into clinically actionable insights. The nano-neuro nexus therefore hypothesizes diagnosis not merely as a linear assay, but rather as an interrelated network of biological, material and data-driven processes functional concurrently. Hence, such integrative perception enables multiplexed, negligibly invasive and temporally resolved detection approaches, placing nanodiagnostics as a systems-level approach for determination of pre-symptomatic condition and precision neuromedicine development.

Although several previously published reviews have summarized nanotechnology-enabled diagnostics for NDDs, however most have primarily focused on individual nanosensor platforms, biomarker categories or translational limitations. In contrast, the present review adopts the ‘nano-neuro nexus’ as an integrative conceptual framework linking neurobiological signal generation, blood–brain barrier mediated signal modulation, nanoscale transduction and AI-assisted computational interpretation. This systems-level approach extends beyond conventional technology-centred analyses by emphasizing the dynamic interplay between biological and analytical layers of neuro-diagnostics. Furthermore, the review uniquely highlights temporally early ‘silent signals’ and incorporates recent advances in AI-integrated nanodiagnostic strategies. Methodologically, the review summarizes the recent advances reported in nanodiagnostic approaches for managing NDDs via a structured analysis of the published literature. Medical Subject Headings (MeSH) terms and keywords (nanodiagnostics, neurodegeneration, biosensors, extracellular vesicles and artificial intelligence) were used alone and in combinations to screen within the databases, such as PubMed/MEDLINE, Embase and Google Scholar. The search was restricted to literature items published since 2009, without regard to language. To find more significant reports, the reference lists of relatable journal articles and review papers were manually examined. Such inclusion criterion was adopted to assure that relevant manuscripts were included for writing the review manuscript. Exclusion criteria included studies unrelated to the research issue, articles without sufficient data or only available as conference abstracts, editorials, comments or opinion. The titles and abstracts of published articles were screened as part of the selection process, and later followed by the evaluation of complete text in agreement with the inclusion and exclusion criteria. The authors were actively engaged in prompt discussion concerning any disputes or ambiguities during the selection process. Through the application of such stringent methodology, the aim was to warrant only relevant and high-quality published reports to be included for the purpose of writing the present semi-systematic review article, thereby offering a summary of the emerging nanodiagnostic strategies.

2 Biological basis of “silent signals” in neurodegeneration

The identification of early, measurable biomarkers termed as “silent signals” or “pre-symptomatic markers”, is fundamental for the technological advancement of nanodiagnostic strategies in detecting NDDs, since such signals highlight the underlying molecular and cellular disturbances leading to explicit neuronal dysfunctions and associated clinical manifestation (Chen et al., 2022; Doroszkiewicz et al., 2022). Centrally aligned to such events is the existence of misfolding, aggregation and propagation of disease-specific proteins, including amyloid-β and tau in AD, α-synuclein in PD and mutant huntingtin in HD. These characteristic proteins undergo conformational transitions causing establishment of toxic oligomers and fibrillar aggregates, which not only leads to cellular homeostasis disruption but also display prion-like seeding behavior, further assisting in their spread across neural networks (Cuanalo-Contreras et al., 2023). Importantly, these pathology promoting species are preferably quantifiable in peripheral biofluids, even though they are present at extremely low levels during the early stages of disease progression, thus recognizing them as principal targets for detection via high-sensitivity nanodiagnostic systems. Beyond aberrant protein/s accumulation, neuroinflammation also embodies additional critical axis of early disease signaling, characterized by microglia and astrocytes activation with concomitant release of pro-inflammatory factors (cytokines and chemokines). Altogether, this highly inflammatory environment significantly contributes to synaptic dysfunctioning and neuronal damage, thereby generating a range of soluble biomarkers that can be detected in blood and CSF (Adamu et al., 2024; Di Benedetto et al., 2022). Simultaneously, oxidative stress and altered mitochondrial dynamics have recently emerged as key contributors to NDDs pathology, mediated by an imbalance between ROS production and antioxidant defense system functioning. This imbalance further leads to lipid peroxidation, DNA damage, including discharge of mitochondrial components (mtDNA and proteins) into circulation, thereby offering additional layers for diagnostic assessment (Mehrotra et al., 2015). Within the nano-neuro nexus, such early biomolecular imperfections establishes the primary signal layer, thus offering initial inputs which nanoscale platforms are efficiently engineered to capture and resolve with maximum fidelity.

Synaptic dysfunctioning as a result of neuronal loss due to alterations in neurotransmitter levels (e.g., dopamine, serotonin, glutamate and acetylcholine) and synaptic protein expression (e.g., SNARE proteins, Synaptotagmin-1) can also be monitored via electrochemical nanosensors (Wei et al., 2022). Moreover, extracellular vesicles (EVs), chiefly exosomes derived from neurons and glial cells (astrocytes, microglia and oligodendrocytes), have gained noteworthy recognition as potent carriers of disease-specific molecular cargo, including proteins, lipids and nucleic acids. Such specialised EVs have demonstrated to circumnavigate the highly-selective blood-brain barrier (BBB), thus allowing them entry into peripheral circulation which serves as accessible substitutions of central nervous system pathology (Jin et al., 2021). The grading of these biomarkers, from their involvement in mediating early molecular misfolding processes to downstream regulation of inflammatory mechanisms and metabolic perturbations, warrants the necessity for establishment of multiplexed detection approaches (such as high sensitivity immunoassays, nano-biosensors and microfluidics), which have demonstrated to be efficient in revealing the dynamic and multifactorial nature of NDDs (Li et al., 2025; Cao et al., 2025). Therefore, nanodiagnostic-based approaches offers a authoritative platform for integrating diverse biomarker signals into a coherent diagnostic framework, thus enabling earlier and more precise detection of disease onset and its progression (Figure 1). Moreover, when observed through the nano-neuro nexus framework, the temporal hierarchy of biomarkers (from initial protein misfolding engagement to downstream initiation of inflammatory and metabolic alterations), defines a precisely stratified signaling architecture that warrants the development of multiplexed and dynamically adaptive diagnostic systems.

3 Smart nanodiagnostic platforms

Within the nano-neuro nexus, nanodiagnostic platforms function as sophisticated transduction layers, in which biological signals are selectively detected, amplified and precisely converted into measureable outputs via engineered nanoscale interfaces. The rapid evolution of nanotechnological advancements has given rise to an assortment of sophisticated smart nanodiagnostic platforms that are distinctively well equipped to identify the low-concentration, heterogeneous biomarkers associated with NDDs (as shown in Table 1), thus overcoming existing limitations of conventional diagnostic approaches (Yadav et al., 2025). One of the rapid advancements is the development of nanoparticle-based biosensors, which influence the exclusive optical, magnetic and electrochemical properties of nanomaterials in attaining high sensitivity and specificity (Li et al., 2022). For instance, gold nanoparticles (AuNPs) demonstrate localized surface plasmon resonance, permitting real-time optical detection of biomolecular interactions with detection limits upto femtomolar levels, whereas magnetic nanoparticles are reported to assist in signal amplification and separation in complex biological samples (Lee et al., 2018; Wang et al., 2024). In addition, Surface-functionalized constructs of AuNPs, have shown femtomolar detection of amyloid-β (Aβ) peptides at femtomolar (10^–15 M) concentrations in complex biological samples such as human plasma, with aggregation-based amplification strategy which further augments their detection sensitivity. Such technologically advanced ultra-sensitive detection approach empowers the distinctive, optical properties of gold nanoparticles, specifically their surface plasmon resonance (SPR) behaviour, which changes significantly upon binding to target Aβ species (Chang et al., 2024; Yano et al., 2019). Moreover, their ease of assembly and compatibility with routine colorimetric assays also support their point-of-care usage. However, their predisposition to non-specific adsorption in biological samples can diminish the accuracy. Primarily, in comparison to more complex systems, gold nanoparticles platforms offer a rational balance between their analytical performance and its translational scalability (Izumi et al., 2025).

TABLE 1. Comparative metrics of existing nanodiagnostic platform.

Furthermore, strengthening or advancement in such systems have been shown by quantum dots (QD), which are semiconductor nanocrystals characterized by size-dependent fluorescence emission including exceptional photostability, with concomitant multiplexed imaging and tracking of protein aggregates and other biomarkers with high spatial and temporal resolution (Vu et al., 2015). Ligand-conjugated QDs has recently shown real-time visualization of protein aggregates with high sensitivity and additional propensity to track protein dynamics, thus supporting multi-target profiling in biofluids. Their resistance to photobleaching makes them suitable for longitudinal imaging. Nonetheless, concerns regarding heavy metal composition and long-term toxicity limit in vivo applicability. Compared with plasmonic systems, QDs excel in multiplexing capacity but face greater barriers to clinical translation (Thal et al., 2020; Gandla et al., 2023).

Correspondingly, plasmonic and Surface-Enhanced Raman Scattering (SERS)-based nanoplatforms have recently being developed as powerful tools for ultra-sensitive detection and identification of molecular signatures (Elsheikh et al., 2024), even at single-molecule resolution via enrichment of Raman scattering signals (Table 1). Such platforms offer the advantage of label-free detection, which subsequently lowers assay complexity, thus empowering direct investigation of biomolecular conformational changes (Esposito et al., 2021). These specialized diagnostic platforms have demonstrated to remarkably differentiate structural variants of misfolded proteins at extremely low concentrations in biological samples, thus offering exceptional molecular specificity. In addition, a recent advance in substrate engineering has further strengthened their detection reliability in biofluids. Such as, AuNPs were electrochemically deposited on an indium tin oxide substrate and Aβ-antibodies were immobilized on the Au-NP-coated ITO substrate, following which interactions between the antigen and the antibody were analyzed using SERS spectroscopy. This biosensor platform showed a strong correlation between the intensity of the SERS signals and the Aβ-antigen concentration, highlighting its potential use for the accurate and sensitive detection of Aβ peptide levels (El-Said et al., 2011; Zhou et al., 2020). Nevertheless, the existence of variability in signal enhancement and fabrication reproducibility still remains a constraint and warrants further standardization for efficient clinical disposition.

Furthermore, nanoelectronic based sensors developed using graphene (particularly graphene oxide and its derivatives) and other two-dimensional materials, additionally expand the diagnostic boundaries by facilitating real-time, label-free detection of biomarkers via detecting changes in electrical conductivity, with prospective applications in continuous monitoring via easily wearable devices (Singh et al., 2022). Such highly functionalized devices have demonstrated remarkable sensitivity in detecting neurotransmitters and protein biomarkers in Parkinson’s and Alzheimer’s, under physiological conditions. The rapid response and integration potential of such platforms with wearable systems make them as attractive choice for continuous real-time monitoring (Zhu et al., 2025). A 3D micro-nanoarchitectured graphene nanosensor reported specific capturing of Aβ oligomers and phosphorylated tau proteins in cerebrospinal fluid, attaining enhanced (2–3 times) detection sensitivity as compared with classical ELISA techniques (Zhou et al., 2020). In addition, assimilation of such nanosensors into microfluidic and lab-on-chip diagnostic systems represent a noteworthy step towards clinical translation, since these platforms offer automated, high-throughput investigation of small sample volumes, thereby aiding in point-of-care diagnostics with subsequently reduced dependency on centralized laboratory infrastructure. Microfluidic systems incorporate nanosensors within miniaturized fluidic networks, enabling controlled, multiplexed biomarker analysis from small biofluids. Such platforms support real-time detection of multiple NDDs biomarkers with significantly reduced assay time and reagent use. Microfluidic chips have shown to serve as powerful platforms in unravelling Alzheimer’s physio-pathological processes. Their compatibility with point-of-care diagnostics enhances their clinical relevance (Lee et al., 2009; Meier et al., 2009).

Markedly, the combining of such advanced nanotechnological platforms suggests the need for further restructuring of nanoplatforms, which are capable enough of simultaneous disease-specific protein detection, signal amplification and concurrent data processing abilities, thus augmenting diagnostic precision and efficiency. Although, there exist limitations in implementation of such platforms due to their reproducibility issues, protocol optimization procedures and performance scalability, therefore warrants extensive validation before their clinical translation. However, the resourcefulness and sensitivity of such smart nanodiagnostic platforms, suggest them as futuristic tools of next-generation diagnostic technologies, possessing the potential to early detect and manage NDDs. Altogether, such technological advancements demonstrate the functional efficiency of nano-neuro nexus via bridging molecular pathology insights with quantifiable signals, thus empowering the transformation of otherwise undetectable biological changes into clinically interpretable data outputs.

4 Blood-brain barrier and central nervous system access

The permeability restriction of BBB represents a challenging obstacle in the context of developing or advancing NDDs diagnostics approaches. BBB acts as a highly selective barrier efficiently regulating the exchange of molecules between the central nervous system (CNS) and peripheral circulation, thus restraining the accessibility of brain-derived biomarkers (Jiao et al., 2024). Within the nano-neuro nexus architecture, the BBB is more aptly considered not uniquely as a restrictive barrier but rather as a dynamic regulatory boundary that precisely governs the release, transformation and peripheral detectability of neurological signals (Blanchette et al., 2025; Ying et al., 2024). This perception is critical for understanding how early pathological changes in the neuro-environment could be efficiently captured via minimally invasive diagnostic approaches (He et al., 2026). In addition to systemic drug administration procedures, intranasal administration of nanoparticles has emerged as a non-invasive method for bypassing the BBB, thus enabling direct access to the CNS via olfactory and trigeminal pathways (Ali et al., 2024; Agosti et al., 2024). This route of drug administration not only heightens its delivery effectiveness but also lowers systemic exposure and associated toxicity. However, such strategies must be judiciously optimized to balance optimum delivery efficiency with safety, since nanoparticles accretion and their habitual off-target effects still are of significant concerns (Badhwar and Haqqani, 2020). In the context of the present manuscript, such strategies are not considered from a therapeutic standpoint alone but rather as conceptual models that demonstrates the feasibility of localized sensing or probe positioning, although their routine applicability in diagnostics still remains limited.

From a diagnostic viewpoint, larger emphasis is being placed on the reverse trajectory, chiefly on the mechanism/s by which brain-derived biomarkers navigate the BBB into peripheral membrane compartments. These primarily include passive diffusion in brain regions with compromised BBB integrity, active transport mechanisms and extracellular vesicle-mediated trafficking. Extracellular vesicles (EVs) chiefly serve as biologically stable carriers encapsulating proteins, nucleic acids and lipids, thus preserving disease-specific molecular signatures during their systemic circulation (Xu et al., 2024). Such specialized transport pathways meticulously govern the concentration, composition and temporal availability of biomarkers (amyloid-β, tau and neurofilament light chain) in peripheral biofluids. Experimental reports also suggest that EVs may cross the BBB via transcytotic routes involving endothelial uptake and vesicular transport, as well as via compromised regions of BBB affected during pathological progression (Banks et al., 2020; Lerussi et al., 2025). In addition, neuron- and glia-derived EVs have been identified in peripheral circulation, thus supporting their potential in effective diagnostic procedures. However, the proportion of circulating EVs that are CNS-derived and the extent to which they retain disease-specific signatures, presently remain subjects of on-going investigation, warranting cautious interpretation in nanodiagnostic applications (Marchetti et al., 2020).

The existence of heterogeneity in BBB permeability across diverse pathological stages of NDDs and among affected individuals introduces a supplementary layer of complexity (Wilhelm et al., 2016). Such diverse variability influences both the efficiency of biomarker concentration and the reliability of peripheral detection, thus highlighting the importance of developing personalized and longitudinal diagnostic approaches. Additionally, a critical consideration in designing a nanodiagnostic system is if peripheral detection is sufficient or rather direct access to CNS compartments is required (Achar et al., 2021). Whereas, blood-based diagnostics offer greater advantages in terms of minimal invasiveness and scalability, however they are constrained by signal attenuation and inter-individual variability. On the other hand, cerebrospinal fluid offers sophisticated fidelity to central pathology determination but is less suitable for routine screening procedures. Emergent approaches involving implantable or minimally invasive nanosensors may enable in situ monitoring in specific conditions, however, their clinical application still remains limited. Presently, investigators continue to elucidate the mechanism/s governing BBB mediated transport and its permeability, since integration of these insights with present nanotechnology platforms holds noteworthy potential to augment the detection and monitoring of NDDs (Song et al., 2021; Gao et al., 2025). Together, the BBB holds a dominant position within the nano-neuro nexus, chiefly functioning as a regulatory interface that governs both the opportunities and constraints of early NDDs diagnostics. Mechanistic insights of BBB-mediated signal dynamics is therefore indispensable for the rational designing and clinical translation of next-generation nanodiagnostic platforms.

5 AI-integrated nanodiagnostics

In context to nano-neuro nexus, application of AI establishes a computational framework which is responsible for decoding complex, high-dimensional outputs generated by engineered nanoscale sensing systems into clinically actionable interpretations. The explicit usage of artificial intelligence (AI)-based approaches with nanodiagnostic platforms represents a noteworthy progression in understanding and efficient utilization of available complex biological datasets, thereby transforming raw nanosensor based outputs into clinically actionable findings (Hassan et al., 2025). Nanodiagnostic systems, mainly those based on techniques such as SERC, fluorescence spectroscopy and electrochemical sensing, are reported to produce high-dimensional datasets which are characterized by precise variations in spectral or electrical signals that are often beyond the resolution offered by classical analytical methods. Machine learning (ML) programmed algorithms, including supervised and unsupervised models, are exceptionally suited to process these high-dimensional datasets, thereby assisting in the identification of patterns and correlations that correspond to specific disease states or corresponding stages of pathological progression (Swanson et al., 2023). For example, deep learning models can be exclusively trained to meticulously differentiate between spectral signals primarily allied with different protein-specific conformations, thus expediting the early detection of circulating misfolded protein species in several NDDs (Ganne et al., 2022). Beyond their regular pattern recognition competency, AI-based models also enable the integration of multi-modal data streams, therefore allowing combination of nanodiagnostic findings with genomic, proteomic and clinical data to generate comprehensive diagnostic profiles. This expertise is chiefly pertinent in the context of NDDs pathological advancement, wherein disease-specific heterogeneity warrants a multi-dimensional approach for its precise diagnosis and prognosis procedures (Mohamed et al., 2026; Hassan et al., 2025). Additionally, predictive modeling based approaches further augments the utility of AI-based nanodiagnostics by offering the estimation of disease risk, disease-associated progression trajectories and therapeutic management responses, hence suggesting personalized medicine options (Dinc and Ardic, 2025). Furthermore, the integration of AI-based models into portable and wearable nanodiagnostic devices has facilitated the real-time monitoring of patient health and data examination, thus paving the way for establishment of decentralized healthcare models (Shajari et al., 2023). Recent technological advancements in AI-integrated nanodiagnostics highlight the convergence of nanosensor outputs with ML-based models for enhanced NDDs detection. Application of convolutional neural networks (CNNs) to SERS spectra derived from plasma samples of Alzheimer’s patients achieved diagnostic accuracies exceeding 90% via capturing subtle conformational signatures of amyloid-β (Kim et al., 2024; Resmi et al., 2024). Also, graphene-based field-effect transistor signals analyzed via support vector machines (SVMs) demonstrated sensitive classification of neurotransmitter imbalances in Parkinsonian cohorts (Abrantes et al., 2022). Similarly, multiplexed QDs-fluorescence datasets processed using Random Forest (RF) algorithms significantly improved multi-biomarker discrimination across different stages of NDDs (Ki et al., 2025), whereas microfluidic platforms upon its integration with ensemble learning models showed a robust early detection of pathology from heterogeneous biomarker panels (Pavel et al., 2025; Gomez et al., 2025). Nevertheless, such approaches still remain vulnerable to overfitting due to their application with limited cohort sizes, including frequently observed challenges in generalizability and interpretability inconsistencies, thereby demanding standardized validation and explainable AI frameworks.

But, the application of AI-based models in nanodiagnostics procedures is not without challenges, which includes concerns related to available data quality, its algorithm transparency and required regulatory approval. The obligatory requirement for large, well-annotated datasets to train robust AI-based models still remains a significant challenge in the healthcare field, so does the necessity for explainable AI systems which offers interpretable results for optimal clinical decision-making processes (Abbas et al., 2025). Regardless of these limitations, the synergistic incorporation of AI and nanotechnology holds significant potential to augment diagnostic accuracy, reduce analytical variability and facilitate earlier detection of NDDs, eventually contributing towards improved patient outcomes. Within the nano-neuro nexus, AI-assisted analytical frameworks may further strengthen the interpretation of biologically derived signals captured via nanoscale transduction platforms, thus enabling more integrated and data-driven neuro-diagnostic strategies. Collectively, these advances could support the development of adaptive and longitudinal diagnostic architectures for improved disease stratification and monitoring, although further clinical validation and standardization is warranted.

6 Translational landscape

The successful translation of nanodiagnostic technologies from laboratory setting to clinical application symbolizes an intricate and multifaceted challenge. This encompasses scientific, regulatory and economic considerations that must be addressed to realize their full potential in managing NDDs. One of the primary obstacles encountered during this translational process is the reproducibility and optimization of nanomaterial synthesis procedures, including their functional characterization (Sharifi et al., 2022). Subsequently, disparity in size, shape, surface chemistry and batch-to-batch uniformity of nanomaterials also holds the propensity to significantly influence nanosensor performance and its corresponding diagnostic accuracy (Cho et al., 2025). Furthermore, the repeated data-output variability confounds the comparison of earlier findings across various investigations and consequently obstructs the establishment of standardized laboratory protocols which are obligatory for their clinical validation. Moreover, the sensitivity and specificity of nanodiagnostic platforms should be meticulously assessed in large, diverse patient cohorts in order to confirm their reliability and generalizability (Tomaszewski et al., 2015). This is particularly, due to the existence of heterogeneity within NDDs in terms of their characteristic pathology, progression and clinical presentation of signs and symptoms. Besides other consideration, nanotoxicology represents another critical factor, since the long-term effects of nanoparticle exposure on biological systems/samples still remains partly understood. Prominent issues such as bioaccumulation, immune system activation and impending interference with normal cellular functioning should be cautiously evaluated via comprehensive pre-clinical and clinical investigations (Bahadar et al., 2016; Abonyi et al., 2025). Similarly, regulatory frameworks for nanodiagnostics usage are still evolving, wherein competent agencies are presently working to frame guidelines for the holistic evaluation and approval of nanotechnology-based medical devices (De Jong et al., 2022; Rehman and Moore, 2021). The non-existence of well-defined regulatory pathways could further delay the translation of promising nanotechnologies and may create ambiguity for developers and investors (Table 2).

TABLE 2. Translational challenges and emerging solutions in nanodiagnostics for neurodegenerative diseases.

In addition, economic factors are also documented to play a significant role in development of a nano-diagnostic product, since the expenditure of developing, manufacturing and implementing nanodiagnostic systems must be balanced against their clinical profits (Bosetti and Jones, 2019; Hemmrich and McNeil, 2024). Although, few nanodiagnostic platforms offer the potential for cost-effective, point-of-care diagnostics, since majority may need specialized equipment and infrastructure requirements, thereby limiting their availability in resource-constrained settings. Therefore, addressing these translational challenges would require substantial coordinated efforts across various disciplines, comprising materials science, clinical research, regulatory science and health economics, to develop scalable, safe and effective nanodiagnostic solutions that could be incorporated into routine clinical practice.

7 Personalized nano-neuromedicine

The rapid advancement of nanodiagnostic platforms has also mediated a move toward developing personalized nano-neuromedicine, a methodology that pursue to modify diagnostic and therapeutic based management strategies towards exclusive molecular and clinical profiles of individual patients (Kaushik et al., 2018; Fornaguera and García-Celma, 2017). It is well known that, NDDs are inherently heterogeneous in nature, exhibiting variations in genetic predisposition, environmental exposures and characteristic pathology progression patterns, thereby demanding advanced diagnostic approaches that could overcome this complexity. Therefore, nanodiagnostic platforms with their inbuilt capacity to precisely distinguish numerous biomarkers simultaneously, even at ultra-low concentrations are preferably suited to generate detailed molecular profiles which reflect explicit pathological state of each individual patient (Yadav et al., 2025). These molecular signatures can further be combined with genomic, transcriptomic and metabolomic datasets to generate holistic individual patient profiles, thus offering existing disease-risk assessment, early diagnosis and suitable therapeutic options. In this scenario, theranostic nanoplatforms have shown a predominantly progressive development in the field by providing combined diagnostic profiles and therapeutic functionalities within a single nano-based platform (Shakeri et al., 2020; Qiao et al., 2023). Such systems are designed to not only identify disease-associated biomarkers but also to precisely deliver targeted therapeutic interventions, hence aiding in real-time monitoring of treatment efficacy and its dynamic adjustment during the course of treatment. For instance, chemically synthesized nanoparticles have demonstrated to specifically bind to misfolded proteins, with concurrent ability to assist as imaging agents and drug delivery carriers, therefore offering both diagnostic and therapeutic options (Panghal and Flora, 2024). The successful amalgamation of nanodiagnostic platforms with digital-health technological platforms has further heightened the potential for futuristic development of personalized nanomedicine procedures, with concomitant monitoring of disease progression via wearable devices and remote data examination (Ahmad et al., 2017).

Regardless of such advances in the research domain, the execution of personalized nano-neuromedicine faces numerous limitations, including the necessity for robust data inclusion, standardization of diagnostic standards and the development of cost-effective solutions that can be made widely available (Wang et al., 2026). Ethical considerations, chiefly related to data confidentiality and the implications of primary disease prediction, must also be carefully addressed. Moreover, it is suggested that, the conjunction of nanotechnology, systems biology and digital health platforms holds the potential to largely transform the management of NDDs, thus shifting the emphasis from generalized treatment strategies to individualized, precision-based personal care (Durgam and Oroszi, 2025).

8 Future directions and emerging frontiers

The future of nanodiagnostics in managing NDDs is believed to be shaped by the convergence of emergent technologies (nanotechnology, AI and advanced biomolecular sensing) that could precisely provide the sensitivity, specificity and real-time monitoring of low-concentration biomarkers, thus allowing a more proactive and predictive approach towards NDD detection (Hassan et al., 2025). Among the most promising recent developments reported in the field is the innovation of single-molecule detection techniques (such as: digital single-molecule immunoassays, nanopore-based sensing, fluorescence techniques and recognition tunneling), which permits the recognition of individual biomolecular events, thereby offering deeper insights into investigating the earliest stages of developing disease pathology (Barman et al., 2025). These advanced approaches, often combined with cutting-edge nanophotonic and plasmonic platforms, have the capacity to detect ultra-low concentrations of nervous system disease-associated biomarkers with near-zero background noise (Zhai et al., 2025). In parallel, the recent development of implantable nanosensors has significantly advanced the field of in-vivo, continuous and real-time monitoring of neural and biochemical parameters, hence offering accurate data on disease advancement and therapeutic management response. Moreover, bio-hybrid nanotechnologies, integrating artificial nanomaterials within biological components (cells or biomolecules), represents another prospective frontier, therefore enabling the foundation and development of highly responsive and adaptive diagnostic systems (Lee and Kim, 2024; Rong et al., 2019). Advances in quantum sensing and nanoscale imaging further augment the capability to visualize and measure molecular dynamics within the brain region, thus offering new opportunities for timely detection and mechanistic insights (Tong et al., 2023). Furthermore, the incorporation of nanodiagnostics with AI and big data analytical approaches is highly expected to promote the enlargement of predictive models that can be utilized to identify individuals at risk of developing NDDs, even long before clinical symptoms could be observed (Hassan et al., 2025).

Nevertheless, such technological advancements also raise significant ethical and societal considerations, predominantly regarding the usage and implementation of predictive diagnostics models and supervision of sensitive health data records. Ensuring equitable accessibility of such advance technologies and addressing impending disparities in healthcare delivery will be critical to their effective implementation (Wasti et al., 2023). As nanotechnological methodologies continue to mature, sustained interdisciplinary collaboration will be essential for refining the translational and clinical applicability of nanodiagnostic platforms in neurodegenerative diseases. Continued advances in nanoscale sensing, biomarker integration, and computational analytics may contribute to more sensitive and longitudinal approaches to disease detection and monitoring, provided that challenges related to reproducibility, validation, and standardization are adequately addressed.

9 Conclusion

Nanodiagnostic technologies represent a rapidly evolving approach for improving the early detection and monitoring of NDDs. By enabling the identification of low-abundance molecular ‘silent signals,’ nanoscale diagnostic platforms may enhance the sensitivity and specificity of current neuro-diagnostic strategies, particularly during pre-clinical and early disease stages. The integration of nanotechnology with AI, microfluidics and systems-level analytical approaches further supports the development of more adaptive and data-driven diagnostic frameworks. Despite substantial technological progress, several challenges still continue to limit clinical translation, including issues related to reproducibility, biosafety, regulatory approval, standardization, cohort heterogeneity and cost-effectiveness. Furthermore, the long-term clinical utility and generalizability of many emerging nanodiagnostic platforms currently remains to be validated through large-scale and longitudinal investigations. Continued interdisciplinary collaboration across nanotechnology, neuroscience, computational biology and clinical medicine will therefore be essential for refining these approaches and improving their translational applicability. Collectively, advances within the nano-neuro nexus hold considerable potential to support earlier disease detection, longitudinal monitoring and more individualized diagnostic strategies in the near future.

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The author(s) declared that generative AI was not used in the creation of this manuscript.

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Footnotes

Edited by: Nitesh Priyadarshi, National Agri-Food Biotechnology Institute, India

Reviewed by: Deniz Özkan Vardar, Lokman Hekim University, Türkiye

Jiahe Li, Sichuan University, China