KOREASCHOLAR

This study aimed to investigate which areas AI is sensitive when inputting panoramic radiographs with dental area masked and when inputting unmasked ones. Therefore, the null hypothesis of this study was that masking dental area would not make a difference in the sensitive areas of osteoporosis determination of AI. For this study 1165 female(average age 48.4 ± 23.9 years) from whom panoramic radiographs were taken were selected. Either osteoporosis or normal should be clearly defined by oral and maxillofacial radiologists. The panoramic radiographs from the female were classified as either osteoporosis or normal according to the mandibular inferior cortex shape. VGG-16 model was used to get training, validating, and testing to determine between osteoporosis or normal. Two experiments were performed; one using unmasked images of panoramic radiographs, and the other using panoramic radiographs with dental region masked. In two experiments, accuracy of VGG-16 was 97.9% with unmasked images and 98.6% with dental-region-masked images. In the osteoporosis group, the sensitive areas identified with unmasked images included cervical vertebrae, maxillary and mandibular cancellous bone, dental area, zygomatic bone, mandibular inferior cortex, and cranial base. The osteoporosis group shows sensitivity on mandibular cancellous bone, cervical vertebrae, and mandibular inferior cortex with masked images. In the normal group, when unmasked images were input, only dental region was sensitive, while with masked images, only mandibular cancellous bone was sensitive. It is suggestive that when dental influence of panoramic radiographs was excluded, AI determined osteoporosis on the mandibular cancellous bone more sensitively.

Ⅰ. INTRODUCTION
Ⅱ. MATERIALS and METHODS
    1. Panoramic radiographs
    2. Deep learning model
    3. Evaluation of deep learning prediction
Ⅲ. RESULTS
Ⅳ. DISCUSSION
REFERENCES

• 안세진(전남대학교 치의학전문대학원) | Sejin Ahn (School of Dentistry, Chonnam National University)
• 송인자(광주여자대학교 간호학과) | In-Ja Song (Department of Nursing, Kwangju Women's University)
• 이재서(전남대학교 치의학전문대학원 구강악안면방사선학교실, 치의학연구소) | Jae-Seo Lee (Department of Oral and Maxillofacial Radiology, School of Dentistry, Dental Science Research Institute, Chonnam National University)
• 이경민(전남대학교 치의학전문대학원 교정학교실, 치의학연구소) | Kyungmin Clara Lee (Department of Orthodontics, 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) Corresponding author
• 송호준(전남대학교 치의학전문대학원 치과재료학교실, 치의학연구소) | Ho-Jun Song (Department of Dental Biomaterials, School of Dentistry, Dental Science Research Institute, Chonnam National University)