Szczegóły publikacji
Opis bibliograficzny
Deep learning for ultrasonographic assessment of temporomandibular joint morphology / Julia LASEK, Karolina Nurzynska, Adam PIÓRKOWSKI, Michał Strzelecki, Rafał Obuchowicz // Tomography [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2379-139X. — 2025 — vol. 11 iss. 3 art. no. 27, s. 1–16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 14–16, Abstr. — Publikacja dostępna online od: 2025-02-27
Autorzy (5)
- AGHLasek Julia
- Nurzynska Karolina
- AGHPiórkowski Adam
- Strzelecki Michał
- Obuchowicz Rafał
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159480 |
|---|---|
| Data dodania do BaDAP | 2025-06-03 |
| Tekst źródłowy | URL |
| DOI | 10.3390/tomography11030027 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Tomography |
Abstract
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 ± 0.08) and joint space (Dice: 0.86 ± 0.09), with notably lower performance for the glenoid fossa (Dice: 0.60 ± 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.
