{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:13:12Z","timestamp":1788415992725,"version":"build-2803163510"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Dent. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chewing difficulty is associated with poorer physical and mental health. Objective measurement of chewing function is currently limited to methods that require specialist lab equipment (for example lab-based manipulation or comminution tests). A remote method for estimating chewing-related metrics would support the development of accessible and scalable means of detecting and monitoring chewing-related health issues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This paper details initial work on developing a novel smartphone-based, image-analysis algorithm to estimate particle-size metrics from photographs of masticated raw carrot deposited in a Petri dish. A two-stage image-processing pipeline was developed, comprising geometric calibration and particle segmentation, enabling estimation of particle-size metrics from smartphone photographs. The algorithm was implemented both as a Python script and as a graphical user interface (GUI), the latter allowing semi-automated per-image adjustment of calibration and segmentation parameters with visual feedback. The script and graphical user interface (GUI) are publicly available: osf.io\/kgx9f<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Performance was evaluated on artificially generated test images to benchmark the algorithm against ground-truth data with known sizes, while also assessing data-collection processes, usability and key challenges. On the more realistic synthetic-particle images, which include overlapping particles of varying size, the algorithm produced a cumulative area error of 20.6% and a mean per-particle relative error of 21.1% (median 14.8%; mean absolute error 1.02 mm\n                      <jats:sup>2<\/jats:sup>\n                      ), with the largest errors occurring for the smallest and most overlapped particles. These synthetic-particle figures reflect performance on successfully matched particles; end-to-end performance including undetected particles would be lower. On simpler geometric-shape images, total-area relative error was lower, at 3.10% for the large-shape set, 3.00% for the small-shape set, and 3.12% across all shapes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The algorithm demonstrates quantifiable analytical performance in estimating chewing-related particle-size metrics from smartphone images, supporting its potential use as a foundation for future remote measurement of chewing function. However, the method is not yet clinically validated. In its current form, the method achieves its best performance through a semi-automated, GUI-based workflow, which introduces a subjective element but provides a practical way to handle heterogeneous image conditions and inform future automation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdmed.2026.1870425","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:21:19Z","timestamp":1788412879000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel image-based algorithm to support future remote assessment of chewing function"],"prefix":"10.3389","volume":"7","author":[{"given":"Dawn","family":"Branley-Bell","sequence":"first","affiliation":[{"name":"School of Psychology, Northumbria University","place":["Newcastle upon Tyne, United 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