A Unified YOLOv8 Approach for Point-of-Care Diagnostics of Salivary α-Amylase
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| Publicat a: | Biosensors vol. 15, no. 7 (2025), p. 421-439 |
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| Autor principal: | |
| Altres autors: | , |
| Publicat: |
MDPI AG
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3233104053 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-6374 | ||
| 024 | 7 | |a 10.3390/bios15070421 |2 doi | |
| 035 | |a 3233104053 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231435 |2 nlm | ||
| 100 | 1 | |a Amin Youssef |u Istituto Italiano di Tecnologia (IIT), Nanobiointeractions & Nanodiagnostics, Via Morego 30, 16163 Genova, Italy; paola.cecere@iit.it | |
| 245 | 1 | |a A Unified YOLOv8 Approach for Point-of-Care Diagnostics of Salivary <i>α</i>-Amylase | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Amylases | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Smartphones | ||
| 653 | |a Color imagery | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Architecture | ||
| 653 | |a Image processing | ||
| 653 | |a Colorimetry | ||
| 653 | |a Automation | ||
| 653 | |a Machine learning | ||
| 653 | |a α-Amylase | ||
| 653 | |a Point of care testing | ||
| 653 | |a Efficiency | ||
| 653 | |a Autonomic nervous system | ||
| 653 | |a Image analysis | ||
| 653 | |a Embedded systems | ||
| 653 | |a Computer vision | ||
| 653 | |a Lighting | ||
| 653 | |a Biomarkers | ||
| 653 | |a Nervous system | ||
| 653 | |a Object recognition | ||
| 653 | |a Real time | ||
| 653 | |a Enzymes | ||
| 653 | |a Data transmission | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Cecere, Paola |u Istituto Italiano di Tecnologia (IIT), Nanobiointeractions &amp; Nanodiagnostics, Via Morego 30, 16163 Genova, Italy; paola.cecere@iit.it | |
| 700 | 1 | |a Pompa, Pier Paolo |u Istituto Italiano di Tecnologia (IIT), Nanobiointeractions &amp; Nanodiagnostics, Via Morego 30, 16163 Genova, Italy; paola.cecere@iit.it | |
| 773 | 0 | |t Biosensors |g vol. 15, no. 7 (2025), p. 421-439 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233104053/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233104053/fulltextwithgraphics/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233104053/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |