Copertina

A Unified YOLOv8 Approach for Point-of-Care Diagnostics of Salivary α-Amylase

Salvato in:
Pubblicato in:Biosensors vol. 15, no. 7 (2025), p. 421-439
Autore principale: Amin Youssef
Altri autori: Cecere, Paola, Pompa, Pier Paolo
Pubblicazione:
MDPI AG
Soggetti:
Accuracy
Amylases
Datasets
Deep learning
Classification
Smartphones
Color imagery
Artificial neural networks
Architecture
Image processing
Colorimetry
Automation
Machine learning
α-Amylase
Point of care testing
Efficiency
Autonomic nervous system
Image analysis
Embedded systems
Computer vision
Lighting
Biomarkers
Nervous system
Object recognition
Real time
Enzymes
Data transmission
Environmental
Accesso online:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract:Salivary <inline-formula>α</inline-formula>-amylase (sAA) is a widely recognized biomarker for stress and autonomic nervous system activity. However, conventional enzymatic assays used to quantify sAA are limited by time-consuming, lab-based protocols. In this study, we present a portable, AI-driven point-of-care system for automated sAA classification via colorimetric image analysis. The system integrates SCHEDA, a custom-designed imaging device providing and ensuring standardized illumination, with a deep learning pipeline optimized for mobile deployment. Two classification strategies were compared: (1) a modular YOLOv4-CNN architecture and (2) a unified YOLOv8 segmentation-classification model. The models were trained on a dataset of 1024 images representing an eight-class classification problem corresponding to distinct sAA concentrations. The results show that red-channel input significantly enhances YOLOv4-CNN performance, achieving 93.5% accuracy compared to 88% with full RGB images. The YOLOv8 model further outperformed both approaches, reaching 96.5% accuracy while simplifying the pipeline and enabling real-time, on-device inference. The system was deployed and validated on a smartphone, demonstrating consistent results in live tests. This work highlights a robust, low-cost platform capable of delivering fast, reliable, and scalable salivary diagnostics for mobile health applications.
ISSN:2079-6374
DOI:10.3390/bios15070421
Fonte:Health & Medical Collection