Wearable Point-of-Care Devices Enabled by Smart Technologies for Early Disease Diagnosis and Biomarker Exploration

Modern diagnostics hinge on the timely identification of disease-specific biomarkers—molecular signatures including proteins, nucleic acids, and metabolites that provide a window into the patient's physiological state. Biomarker identification is central to early disease detection, therapeutic monitoring, and prognosis. With the growing adoption of in vitro diagnostics (IVDs), the integration of biosensing technologies into wearable and point-of-care (POC) platforms has become a focal innovation in precision medicine.

Biomarkers like prostate-specific antigen (PSA), interleukin-6 (IL-6), and C-reactive protein (CRP) are now detectable using compact systems. Electrochemical and optical detection methods—commonly used in ELISA, surface-enhanced Raman spectroscopy (SERS), or lateral flow assays (LFA)—are being miniaturized for seamless incorporation into wearable sensors. The integration of microfluidic platforms with advanced biosensors ensures not only high sensitivity but also multiplexed detection capacity at the patient's side.

Illustrative Overview of Biomarker Detection and Identification Platforms Fig.1 Schematic representation of the biomarker detection and identification platforms. (Haghayegh F., et al., 2024)

Wearable Biosensors: Real-Time, Non-Invasive Disease Monitoring

Skin-Attached Platforms for Sweat, ISF, and Temperature Biomarkers

Wearable sensors have emerged as intelligent devices capable of continuously tracking physiological and biochemical indicators. Platforms such as microneedle patches, epidermal electrochemical sensors, and hydrogel-based sweat patches enable continuous assessment of sweat analytes like cortisol, glucose, and lactate.

Devices such as MicroSweat and Gx Patch utilize colorimetric and electrochemical transduction to measure analytes like chloride and CRP from sweat. Embedded wireless systems transmit real-time data to smartphones for user monitoring and clinician review. Additionally, wearable microneedles allow for interstitial fluid (ISF) sampling, enabling analyte detection with minimal invasiveness and high temporal resolution.

Ocular and Oral Wearables for Tear and Saliva Diagnostics

Smart contact lenses embedded with fluorescent or electrochemical sensors have shown efficacy in monitoring intraocular pressure and detecting glucose and salt concentrations in tear fluid. These ocular biosensors offer non-invasive access to critical metabolic data for conditions like glaucoma and diabetes.

Oral wearable devices—for instance, saliva-based biosensors integrated into dental appliances—allow for the detection of stress hormones and metabolic by-products. These platforms enable continuous sampling and analysis of salivary analytes, including α-amylase, uric acid, and antibodies.

Intelligent Microfluidics: Enabling Lab-Grade Performance On-Body

Microfluidic integration into POC devices enables the miniaturization of complex laboratory functions. Technologies such as capillary-driven channels, valve-controlled chambers, and droplet microreactors allow manipulation of microscale fluid volumes for sample preparation, separation, and reaction.

Advanced systems demonstrated include:

Origami-based paper microfluidics for malaria DNA detection.
ELISA-integrated passive microfluidic chips for SARS-CoV-2 serological analysis.
SERS-based microfluidic assays for cytokine profiling in sepsis diagnostics.

These platforms can manage multiplexed detection with precise reagent control and reduced cross-contamination. They are critical for transitioning complex diagnostic workflows into wearable or portable devices that maintain analytical rigor.

Integration of Artificial Intelligence for Predictive Diagnostics

AI in Signal and Image Analysis

Machine learning (ML) and deep learning (DL) have transformed the landscape of diagnostic analytics. Algorithms now process biosensor data, medical imaging, and clinical signals—such as ECG, EEG, and EMG—to identify disease-specific anomalies with higher accuracy than traditional methods.

For example, convolutional neural networks (CNNs) are employed to detect atrial fibrillation from PPG signals captured by smartwatches. In neurology, DL models have enabled seizure prediction through wearable EEG signal analysis. Explainable AI is increasingly being embedded in wearable platforms to ensure medical interpretability and clinical trust.

AI-Enhanced Bioassay Interpretation

Artificial intelligence enables real-time interpretation of colorimetric, SERS, and fluorescent bioassays. Smartphone-based applications, using ML models such as support vector machines (SVMs) and random forests, interpret spectral data to quantify biomarker concentrations with high precision.

Examples include:

ANN-powered interpretation of glucose levels from colorimetric strips.
RF classifiers for prostate cancer screening using multi-marker urine assays.
CNN-enabled analysis of exosomal profiles via 3D plasmonic nanomembranes for oncology applications.

These integrations significantly reduce interpretation errors and allow for automated triage and decision-making.

Regulatory Trajectory and Commercialization Pathways

The acceleration of AI and wearable diagnostics necessitates alignment with regulatory frameworks. Devices like the Abbott i-STAT TBI test and the Butterfly iQ ultrasound probe exemplify successful pathways through FDA approval. The FDA is now adapting its Software as a Medical Device (SaMD) framework to encompass dynamic AI systems, requiring continuous performance monitoring and post-market evaluation.

The European Union is enacting the Artificial Intelligence Act, with direct implications for AI-powered medical devices. Manufacturers are now required to demonstrate algorithmic transparency, bias mitigation, and performance generalizability across populations.

From Research Labs to Real-World Adoption

Translational Examples and Clinical Use Cases

Several wearable diagnostics have successfully moved from academic prototypes to commercial deployment:

AliveCor's KardiaMobile: FDA-cleared for ECG monitoring, enabling arrhythmia detection through a smartphone app.
EasyScan One: AI-driven malaria detection using microscopy and ML image processing.
Genalyte Maverick^TM: Multi-antigen serology testing with electrochemiluminescence readout and ML analytics.

These cases demonstrate the convergence of advanced sensing, bioinformatics, and real-time computation in deployable diagnostic products.

Omics and Multi-Modal Data Fusion for Biomarker Discovery

High-Dimensional Omics Data Integration

The integration of omics datasets—proteomics, metabolomics, genomics, and transcriptomics—is critical for robust biomarker discovery. ML techniques like principal component analysis (PCA), random forests, and autoencoders allow reduction of data dimensionality and highlight clinically relevant biomolecular patterns.

Examples of applications:

Deep Visual Proteomics (DVP) for colorectal adenoma analysis, revealing MARCKS and DMBT1 as recurrence markers.
AI-driven urinary proteomics for acute kidney injury (AKI) prediction post-angiography with AUC scores above 0.82.
Multi-omics-based IBD classifiers using fecal metagenomics and metabolomics to distinguish between Crohn's disease and ulcerative colitis with an AUC = 0.85.

These findings illustrate the potential of integrated AI-omics platforms for early, accurate disease stratification.

Wearable Diagnostic Devices and Target Biomarkers

Device Type	Biofluid Sampled	Target Biomarkers	Detection Method	Clinical Application
Smart Contact Lens	Tears	Glucose, IOP	Fluorescence, Electrochemical	Diabetes, Glaucoma
Epidermal Sweat Patch	Sweat	Cortisol, CRP, Lactate	Colorimetric, Amperometric	Stress, Sepsis, Metabolic Status
Microneedle ISF Sensor	ISF	Glucose, Sodium	EGT, Potentiometric	Diabetes, Hyponatremia
Smart Diaper	Urine	Glucose, Uric Acid	Electrochemical	Renal Disease, Neonatal Health
Breath VOC Analyzer	Breath	VOCs (e.g., aldehydes)	SERS	COVID-19, Oral Cancer

Future Directions: Personalized, Distributed, Predictive Healthcare

The convergence of wearable diagnostics, AI algorithms, and cloud computing paves the way for predictive and personalized healthcare. Through real-time data streaming and longitudinal monitoring, these platforms are transitioning from reactive diagnostics to proactive health maintenance.

Emerging capabilities include:

Closed-loop therapeutic systems: Integration with drug delivery platforms for autonomous treatment adjustments.
Longitudinal AI analytics: Identification of health trends over time for chronic disease management.
Population-level surveillance: Aggregated data from wearables for epidemiological modeling and outbreak prediction.

Conclusion

The fusion of wearable technology, intelligent diagnostics, and biomarker-driven medicine is catalyzing a transformation in healthcare delivery. This paradigm shift—from centralized labs to distributed, patient-centric platforms—will redefine how diseases are detected, managed, and prevented. With robust regulatory pathways, continued AI innovation, and interdisciplinary collaboration, the vision of personalized, real-time diagnostics is now a practical, scalable reality.

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Reference

Haghayegh, Fatemeh, et al. "Revolutionary point-of-care wearable diagnostics for early disease detection and biomarker discovery through intelligent technologies." Advanced Science 11.36 (2024): 2400595.

This article is for research use only. Do not use in any diagnostic or therapeutic application.