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Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography

Authors
 Ari Lee  ;  Min Su Kim  ;  Sang-Sun Han  ;  PooGyeon Park  ;  Chena Lee  ;  Jong Pil Yun 
Citation
 PLOS ONE, Vol.16(7) : e0254997, 2021-07 
Journal Title
PLOS ONE
Issue Date
2021-07
Abstract
This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.
Files in This Item:
T202102889.pdf Download
DOI
10.1371/journal.pone.0254997
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
Yonsei Authors
Lee, Chena(이채나) ORCID logo https://orcid.org/0000-0002-8943-4192
Han, Sang Sun(한상선) ORCID logo https://orcid.org/0000-0003-1775-7862
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184339
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