Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Development of economical and mass-manufacturable bioelectrodes with desirable physical properties is one of the crucial challenges to realize automated and robust enzymatic biofuel cell (EBFC). This article focuses to develop customized CO₂ laser-induced flexible graphene (LIFG) bioelectrodes on the polyamide substrate. The cost-efficiency and customization of LIFG bioelectrodes have been further demonstrated for EBFC application by integrating them into a microfluidic device, fabricated by the conventional soft lithography on polydimethylsiloxane (PDMS). First, the LIFG bioelectrodes were created at optimized CO₂ laser (power and speed) irradiation, and characterization was performed to ensure the existence of graphene material. Subsequently, the surface morphological study of the noninduced polyamide sheet, LIFG, and LIFG with the relevant enzyme (GOₓ and laccase)-modified bioelectrodes has been characterized. Finally, various voltammetric electrochemical analyses of the modified LIFG bioelectrodes have been accomplished. After such an electrochemical study, the bioelectrodes were integrated into the microfluidic device and a power density of 13 μW/cm² (52 μA/cm²) was harnessed at an optimized fluid rate of 200 μL/min. By using this fabrication method, the proposed LIFG bioelectrodes have shown excellent potential for electrochemical redox and polarization performance in a microfluidic environment, with a huge scope to enrich the power output by introducing the additional cofactor-based electrochemistry and device stacking.

The present work establishes a membraneless, co-laminar flow-based approach to develop a cost-effective microfluidic paper-based analytical device for enzymatic biofuel cell (μPAD-EBFC). The developed μPAD-EBFC supporting the self-capillary fluid transport action, consists of Y-shaped paper microchannel with the fuel (glucose) and oxidant (O2) streaming in parallel over carbon nanotube (CNT) based bucky paper (BP) electrodes modified with biocompatible electrocatalytic enzymes such as Glucose oxidase (GOx) and Laccase without any additional redox cofactor. The electrochemical performance for the modified bioelectrodes i.e. electrocatalytic oxidation and reduction reaction was carried out using Linear Sweep Voltammetry (LSV), Cyclic Voltammetry (CV) and Open Circuit Potential (OCP). The overall performance of μPAD-EBFC was evaluated using polarization studies. Subsequently, the catalytic activity of enzymes on the electrode surface was validated by Scanning Electron Microscope (SEM). This simple and portable μPAD-EBFC can generate the maximum power density to the order of 100 μW/cm2 (600 μA/cm2) at 0.505 V over prolonged durations of around 50 hours. Hence, the presented μPAD-EBFC shows good power density and stability, leading to its strong potential to power miniaturized microelectronics devices and sensors.