{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:06:01Z","timestamp":1790139961395,"version":"4.0.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan","award":["AP25795995"],"award-info":[{"award-number":["AP25795995"]}],"id":[{"id":"https:\/\/ror.org\/03pj6ge82","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Russian Science Foundation","award":["21-72-30024-P"],"award-info":[{"award-number":["21-72-30024-P"]}],"id":[{"id":"https:\/\/ror.org\/03y2gwe85","id-type":"ROR","asserted-by":"crossref"}]},{"award":["21-72-30024-P"],"award-info":[{"award-number":["21-72-30024-P"]}],"id":[{"id":"https:\/\/ror.org\/03y2gwe85","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bios16090528","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:04:17Z","timestamp":1790067857000},"page":"528","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent Distributed Optical Fiber Pressure Sensing for Dental Bite-Force Analysis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6102-4028","authenticated-orcid":false,"given":"Zhanerke","family":"Katrenova","sequence":"first","affiliation":[{"name":"Department of Science and Innovation, Astana IT University, Astana 010000, 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