Emerging trends in amino acid detection: wearable devices and machine learning-assisted signal processing
Clinical Snapshot
PICO Framework
| P — Population | Not formally defined — the review encompasses general populations and disease cohorts (cancer, neurodegenerative disorders, cardiovascular conditions) in whom amino acid biomarker monitoring is clinically relevant |
| I — Intervention | Wearable/flexible sensing devices for non-invasive amino acid detection in biofluids (blood, sweat, interstitial fluid) combined with machine learning signal-processing approaches (chemometric models, deep learning, SVM, ensemble/tree-based models) |
| C — Comparator | Not explicitly stated — implicitly compared against conventional laboratory-based amino acid analysis methods (e.g., HPLC, mass spectrometry) |
| O — Outcomes | Analytical performance metrics of wearable sensors (sensitivity, selectivity, detection limits, operational stability); machine learning model performance in signal interference correction and nonlinear drift compensation; applicability to personalised health monitoring |
Bottom Line
This narrative review from Xiangtan University surveys emerging wearable sensor technologies and machine learning approaches for non-invasive amino acid monitoring — a topic of genuine future clinical interest. However, it does not meet criteria for a systematic review or meta-analysis and should not be interpreted as such. The review lacks a formal search strategy, pre-specified inclusion criteria, quality appraisal of primary studies, and any pooled quantitative synthesis. All performance metrics described reflect individual proof-of-concept studies under controlled laboratory conditions. No clinical validation data in real patient populations are presented, and no patient-centred outcomes are addressed. The CEBM evidence level is best classified as Level 5 (expert narrative review). For practising clinicians, this paper is useful as a horizon-scanning overview of an evolving technological space, but it provides insufficient evidence to inform clinical practice change. Adoption of wearable amino acid monitoring in Australian clinical settings would require prospective clinical validation studies, regulatory approval through the TGA, and integration into RACGP or specialist society guidelines before any recommendation could be made.
Key Findings
P Value: Not reported
Effect Size: Not applicable — no pooled effect size reported; individual device performance metrics (analytical sensitivity, detection limits) are described qualitatively across included studies
Primary Outcome: Narrative synthesis of wearable sensor design principles and ML signal-processing approaches for amino acid detection in biofluids — no primary quantitative outcome is formally defined or pooled
Nnt Or Sensitivity: Not reported in aggregate — individual sensor sensitivity and selectivity values are discussed for specific devices but are not pooled or summarised with uncertainty estimates
Confidence Interval: Not reported — no meta-analytic confidence intervals presented
Clinical Application
Currently low for routine clinical deployment. Technologies described are predominantly at proof-of-concept or early prototype stage. Barriers include sensor stability over extended wear periods, biofluid matrix interference, calibration drift, regulatory approval, manufacturing scalability, and integration with clinical decision-support systems. Real-world analytical performance in diverse patient populations remains undemonstrated. No currently TGA-approved wearable amino acid monitoring devices are available in Australia. The RACGP does not yet have guidelines addressing wearable metabolic biosensors for amino acid monitoring. PBS listing for such technologies is not applicable at this stage. Australian clinical laboratories rely on established methods (LC-MS/MS, HPLC) for amino acid quantification, particularly in newborn screening programs (NICU/metabolic medicine). The technologies reviewed may have future relevance to Australian telehealth and remote monitoring contexts, particularly for Indigenous and rural populations with limited laboratory access, but this requires substantial clinical validation first. Researchers should note that any clinical deployment would require TGA regulatory approval as a Class IIa or higher medical device. Potentially applicable to patients requiring continuous metabolic monitoring — including those with metabolic disorders (phenylketonuria, maple syrup urine disease), oncology patients, individuals with cardiovascular risk, and those with neurodegenerative conditions. However, no clinical validation in defined patient populations is demonstrated in this review.
Abstract
As critical metabolic biomarkers, amino acids exert essential physiological functions, and their abnormal levels are closely associated with a range of diseases such as cancer, neurodegenerative disorders, and cardiovascular conditions. In recent years, amino acid analysis technologies have achieved remarkable advancements in the field of personalized healthcare. This review navigates the latest progress in amino acid analysis, with a focus on two prominent emerging trends. The first trend encompasses the development of flexible and wearable sensing devices for non-invasive, continuous amino acid monitoring in diverse biofluids, such as blood, sweat, and interstitial fluid. Particular attention is given to their design principles, operational mechanisms, practical applications, and key performance metrics. The second trend involves the application of machine learning (ML) for processing and interpreting complex response signals. Specifically, this review discusses how various ML approaches, including classical chemometric linear regression models, deep learning models, support vector machines (SVMs), ensemble learning, and tree-based models, address common challenges in complex environments, such as signal interference and nonlinear drift. Furthermore, this review outlines the current challenges and proposes future research directions, aiming to advance amino acid analysis toward more intelligent, integrated, and personalized health monitoring systems.
References
- 1.Xu, H., He, L., Wei, Y., Chen, Y., & Shu, J. (2026). Emerging trends in amino acid detection: wearable devices and machine learning-assisted signal processing. Analytical Methods: Advancing Methods and Applications. https://doi.org/10.1039/d6ay00596a
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