A Unified YOLOv8 Approach for Point-of-Care Diagnostics of Salivary <i>α</i>-Amylase

A Unified YOLOv8 Approach for Point-of-Care Diagnostics of Salivary <i>α</i>-Amylase

Salivary <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-amylase (sAA) is a widely recognized biomarker for stress and autonomic nervous sys...

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Bibliographic Details
Main Authors: Youssef Amin, Paola Cecere, Pier Paolo Pompa
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Biosensors
Subjects:
α-Amylase
convolutional neural networks (CNNs)
edge computing
image segmentation
machine learning
object detection
Online Access:https://www.mdpi.com/2079-6374/15/7/421
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Summary:Salivary <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></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

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