{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/osteoporosis-screening-leveraging","title":"Osteoporosis screening: Leveraging EfficientNet with complete and cropped facial panoramic radiography imaging","arxiv_id":null,"date":"2024-10-18","proceeding":"Biomedical Signal Processing and Control 2024 10","authors":["Bruno Scholles Soares Dias","Raiza Querrer","Paulo Tadeu Figueiredo","André Ferreira Leite","Nilce Santos de Melo","Lucas Rodrigues Costa","Marcos Fagundes Caetano","Mylene C.Q. Farias"],"abstract":"This paper introduces a novel approach for detecting osteoporosis through the analysis of dental panoramic radiographs (PR) using convolutional neural networks (CNN) based on the EfficientNet architecture. A dataset of PR images from 750 patients was curated, with 579 images showing no osteoporosis (Class C1) and 171 indicating signs of osteoporosis (Class C3). EfficientNet B5, B6, and B7 models were trained to identify osteoporosis in both full PR images and cropped sections focusing on the mandibular cortical region. The models achieved accuracy rates of 95% to 99% using k-fold cross-validation. To validate the models, a new dataset of 60 adult patients who underwent DXA at the lumbar spine and hip was used, with 37 diagnosed with osteoporosis, 22 with normal bone density, and 2 with osteopenia. These patients were invited for PR acquisition, and their PRs were assessed by a general dentist, a radiologist, and the EfficientNet models. Using DXA results as a reference, the CNN models outperformed the dentist in both full and cropped PR evaluations, although the radiologist showed slightly higher accuracy. The study advances computer-aided dental diagnostics by providing an efficient, non-invasive method for osteoporosis screening, with the training and testing codes made publicly available.","url_abs":"https://www.sciencedirect.com/science/article/abs/pii/S1746809424010899?via%3Dihub","url_pdf":"https://www.sciencedirect.com/science/article/abs/pii/S1746809424010899?via%3Dihub","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"osteoporosis-screening-leveraging","repo_url":"https://github.com/BrunoScholles98/Deep-Learning-for-Bone-Health-Classification-through-X-ray-Imaging","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"medical-image-classification","task_name":"Medical Image Classification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"efficientnet","method_name":"EfficientNet"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}