Automated Deep Learning Platform for Early Osteoporosis Risk Assessment from Dental CBCT Scans (OsteoXplore)

Osteoporosis is a common skeletal disease that reduces bone strength and significantly increases the risk of fractures. It is especially prevalent in people over the age of 50 and can lead to disability, reduced quality of life, and increased mortality. Early diagnosis is therefore essential, yet the current gold-standard diagnostic method is not widely accessible and is not suitable for large-scale screening.

The project aims to develop OsteoXplore, an artificial intelligence–based platform that identifies osteoporosis risk using dental cone-beam computed tomography (CBCT) images that are already routinely taken in dental practice. By analysing the thickness and structure of the mandibular cortical bone, the system will automatically detect signs associated with decreased bone mineral density using deep learning techniques. The platform will integrate several image-analysis modules into a single system and will be clinically validated using data from different imaging devices.

This approach would allow dental imaging to be used not only for oral health assessment but also for the early identification of osteoporosis risk. Integrating such a tool into routine dental workflows could help identify at-risk patients earlier and refer them for further diagnosis and treatment.

Project Team

• Anda Slaidiņa
• Assoc. Prof. Laura Neimane
• Maija Slaidiņa
• Laura Krumpāne
• Kaspars Sudars
• Ivars Namatēvs
• Anželika Bureka