Artificial neural network-based biosensors show promise for chronic disease management but face critical hurdles in sensor stability, model generalizability, and system integration.
ANN-based biosensors show promise for early diagnosis of chronic diseases like diabetes and cancer, but require robust clinical validation and standardized frameworks to overcome current limitations in stability and generalizability.
Early diagnosis of chronic diseases represents one of the most significant challenges and opportunities in modern healthcare, with profound implications for improving patient outcomes and alleviating the substantial financial burdens placed on healthcare systems globally. An emerging technological paradigm that promises to address this challenge involves the development of “smart” biosensors. These biosensors are sophisticated analytical devices that integrate advanced machine learning algorithms, particularly artificial neural networks (ANNs), directly with sensing hardware to process complex, multivariate electrochemical and chemiresistive data in real-time. This review critically evaluates ANN-based biosensing systems for chronic disease management, conducting a comprehensive comparison of model architectures across various diagnostic and predictive applications and also highlighting persistent research gaps. While ANNs offer formidable pattern recognition capabilities, their practical performance and clinical utility remain fundamentally constrained by a series of challenges related to data availability, quality, security, and the inherent complexities of biological systems. Our analysis reveals that while substantial progress has been made, particularly in diabetes management and cancer screening via breath analysis, critical hurdles persist in sensor stability, model generalizability, and system integration. Ultimately, this review provides not merely a catalogue of technologies but a comprehensive, critical analysis aimed at equipping researchers with the insights needed to overcome the interdisciplinary barriers to achieving reliable, accessible, and early diagnosis of chronic diseases through next-generation intelligent biosensing platforms. We argue that the path forward requires a concerted focus on generating robust clinical validation data, developing interpretable and trustworthy models, and creating standardized frameworks for system evaluation and deployment.
Leuprech et al. (Mon,) conducted a review in Chronic diseases. Artificial neural network-based biosensors was evaluated. Artificial neural network-based biosensors show promise for chronic disease management but face critical hurdles in sensor stability, model generalizability, and system integration.