Automatic detection and visualization of temporomandibular joint effusion with deep neural network

Autor:	Yeon-Hee Lee, Seonggwang Jeon, Jong-Hyun Won, Q.-Schick Auh, Yung-Kyun Noh
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Temporomandibular joint Temporomandibular disorder Effusion Artificial intelligence Convolutional neural network Deep learning Medicine Science
Zdroj:	Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Druh dokumentu:	article
ISSN:	2045-2322
DOI:	10.1038/s41598-024-69848-9
Popis:	Abstract This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients’ clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p
Databáze:	Directory of Open Access Journals
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