Utilizing Deep Learning to Opportunistically Screen for Osteoporosis from Dental Panoramic Radiographs

Abstract

Osteoporosis, a chronic disease, can be managed through medication and lifestyle changes if detected early. Therefore, there is need for a cost effective method of screening for osteoporosis. In this paper, we propose a deep learning based implementation for developing an automated computer aided diagnostic (CAD) system that harnesses additional information contained in dental panoramic radiographs to detect a person’s risk for developing osteoporosis. Our proposed method follows a two-step approach. First, we apply deep convolutional neural networks (CNNs) to segment key areas of a panoramic radiograph including the mandible, mental foramen, and the mandibular cortical bone. Second, we follow it up with image processing techniques using OpenCV to calculate the ratio of pixels to help arrive at a key ratio called the Panoramic Mandibular Index (PMI). This ratio is instrumental in determining the risk of bone loss in individuals. When compared to the dental clinicians, our model achieved an F1 score of 0.943 on the test set, whereas the performance of dental clinicians was regarded as the standard with a perfect score. Our paper focuses on automating the measurement of PMI to create a CAD system suitable for routine screening of osteoporosis.