This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural networks (CNNs): Alexnet, VGG-16, and GoogLeNet; the performances of the constructed systems were evaluated.

One oral radiologist classified 1500 panoramic radiographs into three types. In C1, the endosteal margin of the cortex was smooth and sharp, whereas porosities were observed in C2 and C3. The risks of osteoporosis were higher in C2 and C3 than in C1; C3 had the highest risk. This information was included with the images as training data; three CNNs were transfer trained. Using each trained CNN, the diagnostic accuracy was assessed using panoramic radiographs and bone mineral density inspection findings in the lumbar spine and femoral neck of 100 additional patients.

All CNNs exhibited relatively good agreement with the oral radiologist’s judgement (86.0%–90.7%). The predictive results of the three systems for osteoporosis of the lumbar spine showed sensitivities of 78.3%–82.6%, specificities of 71.4%–79.2%, and accuracies of 74.0%–79.0%. The predictive results for osteoporosis of the femoral neck showed sensitivities of 80.0%–86.7%, specificities of 67.1%–74.1%, and accuracies of 70.0%–75.0%.

The constructed systems were generally more accurate than the previously developed conventional system. The new systems may facilitate osteoporosis prediction and prevent subsequent fractures by encouraging patients with suspected osteoporosis to undergo further inspections (e.g., dual-energy X-ray absorptiometry) and treatment.