Deep convolutional network is a deep learning approach to optimize image recognition. This study aimed to apply DCNN to the reading of mandibular cortical thinning in digital panoramic radiographs. Digital panoramic radiographs of 1,268 female dental patients (age 45.2 ± 21.1yrs) were used in the reading of the mandibular cortical bone by two maxillofacial radiologists. Among the subjects, 535 normal subject’s panoramic radiographs (age 28.6 ±7.4 yrs) and 533 those of osteoporosis pationts (age 72.1 ± 8.7 yrs) with mandibular cortical thinning were used for training DCNN. In the testing of mandibular cortical thinning, 100 panoramic radiographs of normal subjects (age 26.6 ± 4.5 yrs) and 100 mandibular cortical thinning (age 72.5 ± 7.2 yrs) were used. The sensitive area of DCNN to mandibular cortical thinning was investigated by occluding analysis. The readings of DCNN were compared by two maxillofacial radiologists. DCNN showed 97.5% accuracy, 96% sensitivity, and 99% specificity in reading mandibular cortical thinning. DCNN was sensitively responded on the cancellous and cortical bone of the mandibular inferior area. DCNN was effective in diagnosing mandibular cortical thinning.

• 송지은(전남대학교 치의학전문대학원) | Jieun Song (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)
• Shyam Adhikari(전북대학교 전자공학부)
• 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.) Corresponding author