Academic Journal
Intelligent Distributed Optical Fiber Pressure Sensing for Dental Bite-Force Analysis.
| Τίτλος: | Intelligent Distributed Optical Fiber Pressure Sensing for Dental Bite-Force Analysis. |
|---|---|
| Συγγραφείς: | Katrenova Z; Department of Science and Innovation, Astana IT University, Astana 010000, Kazakhstan., Kussaiyn D; Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan., Alisherov S; Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan., Blanc W; Institute de Physique de Nice, CNRS UMR7010, Université Côte d'Azur, Avenue Joseph Vallot, 06108 Nice, France., Dostavalov A; Institute of Automation and Electrometry, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia.; Physics Department, Novosibirsk State University, Novosibirsk 630090, Russia., Zollanvari A; Department of Electrical and Computer Engineering, Utah Valley University, Orem, UT 84058, USA., Molardi C; Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan. |
| Πηγή: | Biosensors [Biosensors (Basel)] 2026 Sep 21; Vol. 16 (9). Date of Electronic Publication: 2026 Sep 21. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MDPI Pub Country of Publication: Switzerland NLM ID: 101609191 Publication Model: Electronic Cited Medium: Internet ISSN: 2079-6374 (Electronic) Linking ISSN: 20796374 NLM ISO Abbreviation: Biosensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI Pub. |
| Ιατρικοί όροι (MeSH): | Optical Fibers* , Fiber Optic Technology* , Bite Force* , Biosensing Techniques*, Humans ; Machine Learning ; Convolutional Neural Networks ; Boosting Machine Learning Algorithms ; Neural Networks, Computer ; Support Vector Machine ; Soft Computing ; Pressure |
| Περίληψη: | Measuring bite force is essential for assessing the masticatory system and diagnosing oral disease. Existing measurement devices have low spatial resolution and susceptibility to electromagnetic interference. This paper presents a machine learning (ML)-assisted distributed fiber optic sensing system based on Scattering Level Multiplexing (SLMux) for high-resolution bite force analysis. Enhanced backscattered data were acquired through optical backscattered reflectometry from 88 sensing points along the dental arch. Measured data were reconstructed into a two-dimensional map of bite force and analyzed through an ML pipeline. Sector classification across 4 regions and weight prediction were processed by an end-to-end fine-tuned ResNet-18 Convolutional Neural Network (CNN) and classical ML approaches. ResNet-18 is compared with Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost (Extreme Gradient Boosting), Extra Trees, and k-Nearest Neighbors (kNN) trained on handcrafted features. On sector classification, Logistic Regression achieved the best performance (98.71% accuracy). On the weight prediction task, formulated as a 12-class problem, the end-to-end ResNet-18 CNN substantially outperformed all classical models, reaching 51.28% accuracy and a mean absolute error of 78 g, versus 124 g for the best classical model. A regression-based ResNet-18 variant was also trained on the wavelength-shift and weight data, resulting in a mean absolute error of 62.6 g. The results indicate that integrating ML with distributed fiber-optic sensing has the potential to enhance dental diagnostics and treatment planning. |
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| Grant Information: | AP25795995 Ministry of Science and Higher Education of the Republic of Kazakhstan |
| Contributed Indexing: | Keywords: SLMux; dentistry; distributed sensing; intelligent intraoral device; machine learning; mouthguard; optical sensor |
| Entry Date(s): | Date Created: 20260924 Date Completed: 20260924 Latest Revision: 20260926 |
| Update Code: | 20260926 |
| PubMed Central ID: | PMC13604705 |
| DOI: | 10.3390/bios16090528 |
| PMID: | 42783195 |
| Βάση Δεδομένων: | MEDLINE |
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