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Publication

Publication

Automated detection of cyst and tumors of the jaw using deep learning in panoramic radiographs
Journal
Journal of Clinical Medicine
Vol
9(6)
Page
1839
Author
Hyunwoo Yang, Hwiyoung Kim, In-ho Cha, Young-Soo Jung, Woong Nam, Jun young Kim, Jin-Kyu Kim, Yoon Hyeon Kim, Tae Gyeong Oh, Sang-Sun Han, Hyung Jun Kim, and Dongwook Kim
Year
AI-powered Diagnosis
Date
Jun, 2020
File
jcm-09-01839.pdf (3.4M) 2회 다운로드 DATE : 2021-03-15 16:23:00
Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.