Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Unlabelled: The features extracted from diagnostic computed tomography (CT) slices were used to qualitatively detect bone mineral density (BMD) through neural network models, and the evaluation results indicated that it may be a promising approach to perform osteoporosis screening in clinical practice.
Introduction: The purpose of this study is to design a novelty diagnostic method for osteoporosis screening by using the convolutional neural network (CNN), which can be incorporated into the procedure of routine CT diagnostic in medical examination thereby improving the osteoporosis diagnosis and reducing the patient burden.
Methods: The proposed CNN-based method mainly comprises two functional modules to perform qualitative detection of BMD by analyzing the diagnostic 2D CT slice. The first functional module aims to locate and segment the ROI of diagnostic 2D CT slice, called Mark-Segmentation-Network (MS-Net). The second functional module is used to determine the category of BMD by the features of ROI, called BMD-Classification-Network (BMDC-Net). The diagnostic 2D CT slice of pedicle level in lumbar vertebrae (L1) was selected from 3D CT image in our experiments firstly. Then, the trained MS-Net can get the mark image of input original 2D CT slice, thereby obtain the segmentation image. Finally, the trained BMDC-Net can obtain the probability value of normal bone mass, low bone mass, and osteoporosis by inputting the segmentation image. On the basis of network results, the radiologists can provide preliminary qualitative diagnosis results of BMD.
Results: Training of the network was performed on diagnostic 2D CT slices of 150 patients. The network was tested on 63 patients. Each patient corresponds to a 2D CT slice. The proposed MS-Net has an excellent segmentation precision on the shape preservation of different lumbar vertebra. The dice index (DI), pixel accuracy (PA), and intersection over union (IOU) of segmentation results are greater than 0.8. The proposed BMDC-Net achieved an accuracy of 76.65% and an area under the receiver operating characteristic curve of 0.9167.
Conclusions: This study proposed a novel method for qualitative detection of BMD via diagnostic CT slices and it has great potential in clinical applications for osteoporosis screening. The method can potentially reduce the manual burden to radiologists and diagnostic cost to patients.
