{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T07:35:21Z","timestamp":1771572921734,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ. Objective: This study aimed to develop and validate an AI-driven method for the automatic and reproducible measurement of TMJ space width from ultrasonographic images. Methods: A total of 142 TMJ ultrasonographic images were segmented into three anatomical components: the mandibular condyle, joint space, and glenoid fossa. State-of-the-art architectures were tested, and the best-performing 2D Residual U-Net was trained and validated against expert annotations. The algorithm for joint space width measurement based on TMJ segmentation was proposed, calculating the vertical distance between the superior-most point of the mandibular condyle and its corresponding point on the glenoid fossa. Results: The segmentation model achieved high performance for the mandibular condyle (Dice: 0.91 \u00b1 0.08) and joint space (Dice: 0.86 \u00b1 0.09), with notably lower performance for the glenoid fossa (Dice: 0.60 \u00b1 0.24), highlighting variability due to its complex geometry. The TMJ space width measurement algorithm demonstrated minimal bias, with a mean difference of 0.08 mm and a mean absolute error of 0.18 mm compared to reference measurements. Conclusions: The model exhibited potential as a reliable tool for clinical use, demonstrating accuracy in TMJ ultrasonographic analysis. This study underscores the ability of AI-driven segmentation and measurement algorithms to bridge existing gaps in ultrasonographic imaging and lays the foundation for broader clinical applications.<\/jats:p>","DOI":"10.3390\/tomography11030027","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T04:28:18Z","timestamp":1740716898000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2516-1823","authenticated-orcid":false,"given":"Julia","family":"Lasek","sequence":"first","affiliation":[{"name":"Faculty of Geology, Geophysics and Environmental Protection, AGH University of Krakow, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5137-5732","authenticated-orcid":false,"given":"Karolina","family":"Nurzynska","sequence":"additional","affiliation":[{"name":"Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-5322","authenticated-orcid":false,"given":"Adam","family":"Pi\u00f3rkowski","sequence":"additional","affiliation":[{"name":"Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, 30-059 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9102-4929","authenticated-orcid":false,"given":"Micha\u0142","family":"Strzelecki","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Lodz University of Technology, 93-590 Lodz, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5883-5551","authenticated-orcid":false,"given":"Rafa\u0142","family":"Obuchowicz","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, Jagiellonian University Medical College, 30-663 Krakow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1177\/0333102416686302","article-title":"Temporomandibular Disorders: Old Ideas and New Concepts","volume":"37","author":"List","year":"2017","journal-title":"Cephalalgia"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zieli\u0144ski, G., Paj\u0105k-Zieli\u0144ska, B., and Ginszt, M. 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