KOREASCHOLAR

This study aimed to investigate the difference that convolutional neural network(CNN) shows in the determining osteoporosis on panoramic radiograph by performing a paired test by inputting the original image and the limited image including the cortical bone of the posterior border of the mandible used by radiologists. On panoramic radiographs of a total of 661 subjects (mean age 66.3 years ± 11.42), the area including the cortical bone of the posterior part of the mandible was divided into the left and right sides, and the ROI was set, and the remaining area was masked in black to form the limited image. For training of VGG-16, panoramic radiographs of 243 osteoporosis subjects (mean age 72.67 years ± 7.97) and 222 normal subjects (mean age 53.21 years ± 2.46) were used, and testing 1 and testing 2 were performed on the original and limited images, respectively, using panoramic radiographs of 51 osteoporosis subjects (mean age 72.78 years ± 8.3) and 47 normal subjects (mean age 53.32 years ± 2.81). The accuracy of VGG-16 for determining osteoporosis was 97%, in the testing 1 and 100% in the testing 2. When determining osteoporosis on the original image, CNN showed sensitivity in a wide range of areas including not only the inferior cortical bone of the mandible but also the maxillary and mandibular cancellous bone, cervical spine, and zygomatic bone. When the same ROI including the lower inferior cortical border of the mandible of the osteoporosis group was applied and the sensitive region was compared between the original image and the limited image, the original image showed wider sensitive region in cancellous bone and cortical bone than on the limited image (p<.05). Since osteoporosis is a disease that affects throughout the skeletal system, this finding seems very valid.

Abstract
Ⅰ. INTRODUCTION
Ⅱ. MATERIALS and METHODS
    1. 데이터 준비
    2. 딥러닝 training 및 testing
    3. 통계분석
Ⅲ. RESULTS
Ⅳ. DISCUSSION
REFERENCES

• 최희원(전남대학교 치의학전문대학원) | Heewon Choi (School of Dentistry, Chonnam National University)
• 송인자(광주여자대학교 간호학과) | In-Ja Song (Department of Nursing, Kwangju Women's 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) Corresponding author
• 송호준(전남대학교 치의학전문대학원 치과재료학교실, 치의학연구소) | Ho-Jun Song (Department of Dental Biomaterials, School of Dentistry, Dental Science Research Institute, Chonnam National University)