KOREASCHOLAR

This study was performed as a part of serial experiments of applying convolutional neural network(CNN) in determining osteoporosis on panoramic radiograph. The purpose of this study was to investigate how sensitively CNN determine osteoporosis on cropped panoramic radiograph. Panoramic radiographs from 1268 female patients(mean age 45.2 ± 21.1 yrs) were selected for this study. For the osteoporosis group, 633(mean age 72.2 ± 8.5 yrs) were selected, while for the normal group 635(mean age 28.3 ± 7.0 yrs). AlexNet was utilized as CNN in this study. A multiple-column CNN was designed with two rectangular regions of interest on the mandible inferior area. An occluding method was used to analyze the sensitive area in determining osteoporosis on AlexNet. Testing of AlexNet showed accuracy of 99% in determining osteoporosis on panoramic radiographs. AlexNet was sensitive at the area of cortical and cancellous bone of the mandible inferior area including adjacent soft tissue.

• 배수영(전남대학교 치의학전문대학원) | Suyoung Bae (School of Dentistry, Chonnam National University)
• 송인자(광주여자대학교 간호학과) | In-Ja Song (Department of Nursing, Kwangju Women's University)
• 김형석(전북대학교 전자공학부) | Hyongsuk Kim (Chonbuk National University Electronic engineering)
• 이재서(전남대학교 치의학전문대학원 구강악안면방사선학교실/치의학연구소) | Jae-Seo Lee (Department of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University)
• 윤숙자(전남대학교 치의학전문대학원 구강악안면방사선학교실/치의학연구소) | Suk-Ja Yoon (Department of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University)
• 정호걸(인비젼랩) | Ho-Gul Jeong (InvisionLab Inc.) Correspondence