Work

Use of neural networks to predict a potential osteoporotic metabolic condition based on the panoramic mandibular index

Year: 2025

Type: article

Abstract: OBJECTIVE: This study investigates the use of neural networks to predict potential osteoporotic metabolic conditions using the Panoramic Mandibular Index (PMI) derived from panoramic radiographs. STUDY DESIGN: A total of n = 750 radiographs were collected and 1,500 regions of interest (ROIs) were analysed. Convolutional neural network (CNN), specifically ResNet-50, were used with transfer learning, achieving the highest accuracy. Three classification approaches were tested: binary classification (PMI > 0.25 vs. PMI < 0.25), 3-class classification (PMI > 0.25, 0.2-0.25, PMI < 0.2), and a modified 2-class classification (PMI > 0.25 vs. PMI < 0.2). RESULTS: The best classification, the modified 2-class classification, achieved an accuracy of 89.5%. Data augmentation techniques including rotation, reflection, and scaling, improved model robustness. CONCLUSIONS: This study demonstrates that a neural network can be trained to effectively classify PMI values in panoramic radiographs, providing an easy applicable and noninvasive method to identify patients at risk of osteoporosis.

Source: Oral Surgery Oral Medicine Oral Pathology and Oral Radiology

DOI: 10.1016/j.oooo.2025.09.022

Authors: Philip Roebers, Ralf Schulze

Institution: University of Bern

Language: English

Topic: Dental Radiography and Imaging

Subfield: Oral Surgery

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,670