Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study

OBJECTIVES: To evaluate the diagnostic performance of a deep convolutional neural network (DCNN) -based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists. METHODS: Oral and maxillofacial radiologists with >10 years of experience reviewed the panoramic radiographs of 1268 females mean ± standard deviation (SD) age: 52. 5 ± 22. 3 years and made a diagnosis of osteoporosis when cortical erosion of the mandibular inferior cortex was observed. Among the females, 635 had no osteoporosis mean (± SD) age: 32. 8 ± SD 12. 1 years and 633 had osteoporosis (72. 2 ± 8. 5 years). All panoramic radiographs were analysed using three CAD systems, single-column DCNN (SC-DCNN), single-column with data augmentation DCNN (SC-DCNN Augment) and multicolumn DCNN (MC-DCNN). Among the radiographs, 200 panoramic radiographs mean (± SD) patient age: 63. 9 ± 10. 7 years were used for testing the performance of the DCNN in detecting osteoporosis in this study. The diagnostic performance of the DCNN-based CAD system was assessed by receiver operating characteristic (ROC) analysis. RESULTS: The area under the curve (AUC) values obtained using SC-DCNN, SC-DCNN (Augment) and MC-DCNN were 0. 9763, 0. 9991 and 0. 9987, respectively. CONCLUSIONS: The DCNN-based CAD system showed high agreement with experienced oral and maxillofacial radiologists in detecting osteoporosis. A DCNN-based CAD system could provide information to dentists for the early detection of osteoporosis, and asymptomatic patients with osteoporosis can then be referred to the appropriate medical professionals.