Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening

Abstract

There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. Methods: The present study reports the application of a neural network classifier to the processing of previously collected data on very low power radiofrequency propagation through the wrist to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low Bone Mineral Density (BMD), characterized by a Dual X-Ray Absorptiometry (DXA) T-score below -1, measured within one year. Group 2 included 40 healthy and mostly young subjects without major clinical risk factors such as a (family) history of bone fracture. We process the complex radiofrequency spectrum from 30 kHz to 2 GHz. Instead of averaging data for both wrists, we process them independently along with the wrist circumference and then combine the results, which greatly increases the sensitivity. Measurements along with data processing require less than 1 min. Results: For the two dichotomic groups identified above, the neural network classifier of the radiofrequency spectrum reports a sensitivity of 83% and a specificity of 94%. Significance: These results are obtained without including any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. This approach has the potential for screening patients at risk for fragility fractures in the office, given the ease of implementation, small device size, and low costs associated with both the technique and the equipment.

Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis

“…In addition to DXA, in 2021, J.W. Adams et al proposed the application of neural networks for screening application analysis in osteoporosis [14], which utilizes low-frequency radiofrequency data passing through the wrist and uses a multilayer perceptron (MLP) to do the analysis, and in 2020, B. Zhang et al proposed to train a CNN model based on lumbar spine X-ray images to read osteoporosis and bone loss [15], which are also of great help for orthopedic applications. In 2020, N. Yamamoto et al used ResNet, GoogLeNet, and EfficientNet to classify hip X-ray images for osteoporosis [16].…”

Imaging of Metabolic Bone Diseases: The Spine View, Part II

“…To depict regional osteoporosis, data sets considered include plain radiographs, 34 CT, 35 36 radiofrequency propagation at the wrist, 37 38 DXA, 36 or US. For total whole-body assessments, data sets from DXA imaging have been examined.…”