Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs

Hyunwoo Yang, Eun Jo, Hyung Jun Kim, In Ho Cha, Young Soo Jung, Woong Nam, Jun Young Kim, Jin Kyu Kim, Yoon Hyeon Kim, Tae Gyeong Oh, Sang Sun Han, Hwiyoung Kim, Dongwook Kim

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)


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 cyst. 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.

Original languageEnglish
Article number1839
Pages (from-to)1-14
Number of pages14
JournalJournal of Clinical Medicine
Issue number6
Publication statusPublished - 2020 Jun

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Medicine(all)


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