Machine Learning-Assisted 3D Flexible Organic Transistor for High-Accuracy Metabolites Analysis and Other Clinical Applications
Year: 2024
Type: article
Abstract: The integration of advanced diagnostic technologies in healthcare is crucial for enhancing the accuracy and efficiency of disease detection and management. This paper presents an innovative approach combining machine learning-assisted 3D flexible fiber-based organic transistor (FOT) sensors for high-accuracy metabolite analysis and potential diagnostic applications. Machine learning algorithms further enhance the analytical capabilities of FOT sensors by effectively processing complex data, identifying patterns, and predicting diagnostic outcomes with 100% high accuracy. We explore the fabrication and operational mechanisms of these transistors, the role of machine learning in metabolite analysis, and their potential clinical applications by analyzing practical human blood samples for hypernatremia syndrome. This synergy not only improves diagnostic precision but also holds potential for the development of personalized diagnostics, tailoring treatments for individual metabolic profiles.
Source: Chemosensors
Authors: Caizhi Liao, Huaxing Wu, Luigi G. Occhipinti
Institutions: Sun Yat-sen University, University of Cambridge
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,069
Awards: EPSRC Centre for Innovative Manufacturing in Large Area Electronics, Smart Flexible Quantum Dot Lighting, Design theory-based nanostructured leaf-vein networks for selective VOC sensing, G3485915164, G3831781367, G3909464609, G4227321859, EPSRC Centre for Doctoral Training in Graphene Technology, G513766455, G5367623111, G6613493462, G730709649, G766258362, G8757997133
OpenAlex ID: W4402134652