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Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging

Authors
 Jae-Young Kim  ;  Dongwook Kim  ;  Kug Jin Jeon  ;  Hwiyoung Kim  ;  Jong-Ki Huh 
Citation
 SCIENTIFIC REPORTS, Vol.11(1) : 6680, 2021-03 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2021-03
Abstract
The goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previously reported results. The study objects were obtained by reviewing medical records from January 2005 to June 2018. 299 joints from 289 patients were divided into perforated and non-perforated groups based on the existence of disc perforation confirmed during surgery. Experienced observers interpreted the TMJ MRI images to extract features. Data containing those features were applied to build and validate prediction models using random forest and multilayer perceptron (MLP) techniques, the latter using the Keras framework, a recent deep learning architecture. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the performances of the models. MLP produced the best performance (AUC 0.940), followed by random forest (AUC 0.918) and disc shape alone (AUC 0.791). The MLP and random forest were also superior to previously reported results using MRI (AUC 0.808) and MRI-based nomogram (AUC 0.889). Implementing deep learning showed superior performance in predicting disc perforation in TMJ compared to conventional methods and previous reports.
DOI
10.1038/s41598-021-86115-3
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Dong Wook(김동욱) ORCID logo https://orcid.org/0000-0001-6167-6475
Kim, Jae Young(김재영) ORCID logo https://orcid.org/0000-0002-9423-438X
Kim, Hwiyoung(김휘영)
Jeon, Kug Jin(전국진) ORCID logo https://orcid.org/0000-0002-5862-2975
Huh, Jong Ki(허종기) ORCID logo https://orcid.org/0000-0002-7381-3972
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182865
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