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Development and Validation of a Visually Explainable Deep Learning Model for Classification of C-shaped Canals of the Mandibular Second Molars in Periapical and Panoramic Dental Radiographs

 Sujin Yang  ;  Hagyeong Lee  ;  Byounghan Jang  ;  Kee-Deog Kim  ;  Jaeyeon Kim  ;  Hwiyoung Kim  ;  Wonse Park 
 JOURNAL OF ENDODONTICS, Vol.48(7) : 914-921, 2022-07 
Journal Title
Issue Date
Cone-Beam Computed Tomography / methods ; Deep Learning* ; Dental Pulp Cavity / diagnostic imaging ; Humans ; Mandible / diagnostic imaging ; Molar / diagnostic imaging ; Tooth Root*
C-shaped canal ; computer vision ; convolutional neural networks ; deep learning ; machine learning ; mandibular second molar
Introduction: The purpose of this study was to develop and validate a visually explainable deep learning model for the classification of C-shaped canals of the mandibular second molars in dental radiographs.

Methods: The periapical and panoramic images of 1000 mandibular second molars were collected from 372 patients. The diagnostic performance of the deep learning system using periapical and panoramic radiographs was investigated in respect to its ability to determine whether the second mandibular molar showed a C-shaped canal configuration. The assessment of the canal configuration of cone-beam computed tomographic volumes from 372 patients (740 mandibular second molars) was used as a gold standard.

Results: The deep convolutional neural network algorithm model showed high accuracy in predicting the C-shaped canal variation among mandibular second molars in both periapical and panoramic images. The model demonstrated best results when using image patches including only the root portion of the tooth and when using both periapical and panoramic images for training (area under the curve [AUC] = 0.99). The model's diagnostic performance using only the root portion of the tooth (AUC: periapical = 0.98 and panoramic = 0.95) was similar to a specialist (AUC: periapical = 0.95 and panoramic = 0.96) and better than a novice general clinician (AUC: periapical = 0.89 and panoramic = 0.91). Both the specialist and general clinician showed better diagnostic performance when reading panoramic radiographs compared with periapical images.

Conclusions: With further optimization of the test data using a larger data set and improvements made in the model, a deep learning system may be expected to effectively diagnose C-shaped canals and aid clinicians in practice and education.
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1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Advanced General Dentistry (통합치의학과) > 1. Journal Papers
Yonsei Authors
Kim, Kee Deog(김기덕) ORCID logo https://orcid.org/0000-0003-3055-5130
Kim, Hwiyoung(김휘영)
Park, Wonse(박원서) ORCID logo https://orcid.org/0000-0002-2081-1156
Yang, Sujin(양수진)
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