Amin Youssef, Cecere Paola, Pompa Pier Paolo
Istituto Italiano di Tecnologia (IIT), Nanobiointeractions & Nanodiagnostics, Via Morego 30, 16163 Genova, Italy.
College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China.
Biosensors (Basel). 2025 Jul 2;15(7):421. doi: 10.3390/bios15070421.
Salivary α-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.
唾液α-淀粉酶（sAA）是一种广泛认可的应激和自主神经系统活动生物标志物。然而，用于定量sAA的传统酶促检测方法受限于耗时的实验室操作流程。在本研究中，我们提出了一种便携式、人工智能驱动的即时检测系统，通过比色图像分析实现sAA的自动分类。该系统将SCHEDA（一种定制设计的成像设备，可提供并确保标准化照明）与针对移动部署优化的深度学习管道集成在一起。比较了两种分类策略：（1）模块化YOLOv4-CNN架构和（2）统一的YOLOv8分割-分类模型。这些模型在一个包含1024张图像的数据集上进行训练，该数据集代表了一个对应于不同sAA浓度的八类分类问题。结果表明，红色通道输入显著提高了YOLOv4-CNN的性能，与全RGB图像的88%准确率相比，达到了93.5%的准确率。YOLOv8模型进一步超越了这两种方法，达到了96.5%的准确率，同时简化了流程并实现了实时设备上推理。该系统在智能手机上进行了部署和验证，在现场测试中显示出一致的结果。这项工作突出了一个强大、低成本的平台，能够为移动健康应用提供快速、可靠且可扩展的唾液诊断。
