Learned Electrochemical Sensor Devices (LeSD) for Glucose Predictions: A Mini Review and Meta-Analytic Synthesis
Abstract
The integration of machine learning (ML) with advanced electrochemical sensors (LeSD) is creating a paradigm shift in disease diagnosis, by enabling minimally invasive to noninvasive biomarker predictions, with high prediction accuracies, robustness to environmental variabilities. Such LeSDs provide easy to access, portable, miniaturized, wearable platforms for biomarker detection and its level quantification. This minireview synthesizes recent studies employing diverse ML algorithms across enzymatic, non-enzymatic, wearable, and neuromorphic sensor types, specifically for predicting glucose and thus, diabetic diagnosis. The review follows a meta-analysis perspective, evaluating pooled accuracy effects and highlights the sources of heterogeneity. In addition, challenges and future research directions for effective clinical translation of advancements in LeSDs are discussed. With its in-depth coverage of recent advancements in LeSD realizations, this review could serve as a valuable information resource for both clinicians and researchers.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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