A Deep Learning Approach for Osteoporosis Identification using Cone-beam Computed Tomography

The goal of the interdisciplinary project is to develop an innovative method for the identification the risk of osteoporosis by using Cone-beam Computed Tomography (CBCT) of the maxillofacial region and to evaluate its efficacy by using an end-to-end Deep Learning (DL) approach. CBCT examination is a non-invasive x-ray technology which produces 3D images. Using a DL approach, a Computer Vision method will be elaborated which can identify more quickly and accurately the risk of osteoporosis in women. Consequently, it facilitates the early treatment of the disease as well as prevents osteoporotic fractures. The project will aim for the expansion of personalized medicine, medical and ICT sectors. The project will be conducted by the medical experts from the Rīga Stradiņš University (RSU) and DL researchers from the Institute of Electronics and Computer Sciences (EDI). The patient`s dataset will be collected by RSU researchers, in which CBCT and osteodensitometry studies are planned for 220 patients. The various measurements of the quality and quantity of the bone and radiological bone density in the maxilla and cervical vertebrae will be performed. The results will be used by EDI to develop a computer-based method of semantic segmentation, classification and explainability of osteoporosis. The project will result in 3 scientific articles, 3 presentations for international conferences and developing guidelines for dentists to determine the risk of osteoporosis by using CBCT.