所属 歯学部 歯科放射線学 職種 助教
|標題||Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique.|
|掲載誌名||Oral Surg Oral Med Oral Pathol Oral Radiol.|
|著者・共著者||Ariji Y, Yanashita Y, Kutsuna S, Muramatsu C, Fukuda M, Kise Y, Nozawa M, Kuwada C, Fujita H, Katsumata A, Ariji E.|
The aim of this study was to investigate whether a deep learning object detection technique can automatically detect and classify radiolucent lesions in the mandible on panoramic radiographs.
Panoramic radiographs of patients with mandibular radiolucent lesions of 10 mm or greater, including ameloblastomas, odontogenic keratocysts, dentigerous cysts, radicular cysts, and simple bone cysts, were included. Lesion labels, including region of interest coordinates, were created in text format. In total, 210 training images and labels were imported into the deep learning GPU training system (DIGITS).
Sensitivity was 0.88 for both testing 1 and 2. The false-positive rate per image was 0.00 for testing 1 and 0.04 for testing 2. The best combination of detection and classification sensitivity occurred with dentigerous cysts.
Radiolucent lesions of the mandible can be detected with high sensitivity using deep learning.