Teruhiko Hiraiwa1, Yoshiko Ariji1, Motoki Fukuda1, Yoshitaka Kise1, Kazuhiko Nakata2, Akitoshi Katsumata3, Hiroshi Fujita4, Eiichiro Ariji1. 1. 1 Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry , Nagoya , Japan. 2. 2 Department of Endodontics, Aichi-Gakuin University School of Dentistry , Nagoya , Japan. 3. 3 Department of Oral Radiology, Asahi University School of Dentistry , Mizuho , Japan. 4. 4 Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University , Gifu , Japan.
Abstract
OBJECTIVES: : The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. METHODS: : CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. RESULTS: : Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. CONCLUSIONS: : The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
OBJECTIVES: : The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. METHODS: : CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. RESULTS: : Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. CONCLUSIONS: : The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
Entities:
Keywords:
panoramic radiography; artificial intelligence,; deep learning; mandibular first molar; root morphology
Authors: Manal H Hamdan; Lyudmila Tuzova; André Mol; Peter Z Tawil; Dmitry Tuzoff; Donald A Tyndall Journal: Dentomaxillofac Radiol Date: 2022-09-12 Impact factor: 3.525