{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:08:08Z","timestamp":1785510488085,"version":"3.56.0"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005375","name":"Latvian Council of Science","doi-asserted-by":"publisher","award":["lzp-2021\/1-0031"],"award-info":[{"award-number":["lzp-2021\/1-0031"]}],"id":[{"id":"10.13039\/501100005375","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans of the mandible. The evaluation was conducted using 188 patients\u2019 mandibular CBCT images utilizing DCNN models built on the ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused on mandibular bone slice identification, Stage 2 pinpointed the coordinates for mandibular bone cross-sectional views, and Stage 3 computed the mandibular bone\u2019s thickness, highlighting osteoporotic variances. The procedure, built using ResNet-101 networks, showcased efficacy in osteoporosis detection using CBCT scans: Stage 1 achieved a remarkable 98.85% training accuracy, Stage 2 minimized L1 loss to a mere 1.02 pixels, and the last stage\u2019s bone thickness computation algorithm reported a mean squared error of 0.8377. These findings underline the significant potential of AI in osteoporosis identification and its promise for enhanced medical care. The compartmentalized method endorses a sturdier DCNN training and heightened model transparency. Moreover, the outcomes illustrate the efficacy of a modular transfer learning method for osteoporosis detection, even when relying on limited mandibular CBCT datasets. The methodology given is accompanied by the source code available on GitLab.<\/jats:p>","DOI":"10.3390\/tomography9050141","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T04:15:38Z","timestamp":1695615338000},"page":"1772-1786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-5558","authenticated-orcid":false,"given":"Ivars","family":"Namatevs","sequence":"first","affiliation":[{"name":"Institute of Electronics and Computer Science, LV-1006 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3356-4764","authenticated-orcid":false,"given":"Arturs","family":"Nikulins","sequence":"additional","affiliation":[{"name":"Institute of Electronics and Computer Science, LV-1006 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5329-4426","authenticated-orcid":false,"given":"Edgars","family":"Edelmers","sequence":"additional","affiliation":[{"name":"Institute of Electronics and Computer Science, LV-1006 Riga, Latvia"},{"name":"Department of Morphology, Institute of Anatomy and Anthropology, R\u012bga Stradi\u0146\u0161 University, LV-1010 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5411-5810","authenticated-orcid":false,"given":"Laura","family":"Neimane","sequence":"additional","affiliation":[{"name":"Department of Conservative Dentistry and Oral Health, Institute of Stomatology, R\u012bga Stradi\u0146\u0161 University, LV-1007 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8353-4187","authenticated-orcid":false,"given":"Anda","family":"Slaidina","sequence":"additional","affiliation":[{"name":"Department of Prosthetic Dentistry, Institute of Stomatology, R\u012bga Stradi\u0146\u0161 University, LV-1007 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2443-9582","authenticated-orcid":false,"given":"Oskars","family":"Radzins","sequence":"additional","affiliation":[{"name":"Department of Orthodontics, Institute of Stomatology, R\u012bga Stradi\u0146\u0161 University, LV-1007 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9110-9065","authenticated-orcid":false,"given":"Kaspars","family":"Sudars","sequence":"additional","affiliation":[{"name":"Institute of Electronics and Computer Science, LV-1006 Riga, Latvia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/s41746-020-0249-z","article-title":"Personalized Predictions of Patient Outcomes during and after Hospitalization Using Artificial Intelligence","volume":"3","author":"Hilton","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1016\/j.csbj.2020.08.019","article-title":"Artificial Intelligence (AI) and Big Data in Cancer and Precision Oncology","volume":"18","author":"Dlamini","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"(1993). Consensus Development Conference: Diagnosis, Prophylaxis, and Treatment of Osteoporosis. Am. J. Med., 94, 646\u2013650.","DOI":"10.1016\/0002-9343(93)90218-E"},{"key":"ref_4","unstructured":"Organisation mondiale de la sant\u00e9 (2006). Working Together for Health: The World Health Report 2006, World Health Organization. The World Health Report."},{"key":"ref_5","first-page":"e29367","article-title":"Postmenopausal Osteoporosis: A Literature Review","volume":"14","author":"Bhatnagar","year":"2022","journal-title":"Cureus"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s11657-017-0370-z","article-title":"The Impact of Comorbidities on Hip Fracture Mortality: A Retrospective Population-Based Cohort Study","volume":"12","author":"Raag","year":"2017","journal-title":"Arch. Osteoporos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1007\/s001980170015","article-title":"Impact of Hip and Vertebral Fractures on Quality-Adjusted Life Years","volume":"12","author":"Tosteson","year":"2001","journal-title":"Osteoporos. Int."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11282-021-00518-6","article-title":"Clinical Guidelines for the Application of Panoramic Radiographs in Screening for Osteoporosis","volume":"37","author":"Taguchi","year":"2021","journal-title":"Oral Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"(2010). The Editor IOF World Congress on Osteoporosis & 10th European Congress on Clinical and Economic Aspects of Osteoporosis and Osteoarthritis: Poster Presentation Abstracts. Osteoporos. Int., 21, 25\u2013388.","DOI":"10.1007\/s00198-010-1247-9"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Issrani, R., Prabhu, N., Sghaireen, M.G., Ganji, K.K., Alqahtani, A.M.A., ALJamaan, T.S., Alanazi, A.M., Alanazi, S.H., Alam, M.K., and Munisekhar, M.S. (2022). Cone-Beam Computed Tomography: A New Tool on the Horizon for Forensic Dentistry. Int. J. Environ. Res. Public. Health, 19.","DOI":"10.3390\/ijerph19095352"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.oooo.2021.06.014","article-title":"Gray Values of the Cervical Vertebrae Detected by Cone Beam Computed Tomography for the Identification of Osteoporosis and Osteopenia in Postmenopausal Women","volume":"133","author":"Slaidina","year":"2022","journal-title":"Oral Surg. Oral Med. Oral Pathol. Oral Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Slaidina, A., Springe, B., Abeltins, A., Uribe, S.E., and Lejnieks, A. (2023). The Effect of General Bone Mineral Density on the Quantity and Quality of the Edentulous Mandible: A Cross-Sectional Clinical Study. Dent. J., 11.","DOI":"10.3390\/dj11010017"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17S","DOI":"10.1177\/0022034514554949","article-title":"Panoramic Measures for Oral Bone Mass in Detecting Osteoporosis: A Systematic Review and Meta-Analysis","volume":"94","author":"Calciolari","year":"2015","journal-title":"J. Dent. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1007\/s00198-021-05852-3","article-title":"Use of Fractal Analysis in Dental Images for Osteoporosis Detection: A Systematic Review and Meta-Analysis","volume":"32","author":"Franciotti","year":"2021","journal-title":"Osteoporos. Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20210365","DOI":"10.1259\/dmfr.20210365","article-title":"Is Jaw Fractal Dimension a Reliable Biomarker for Osteoporosis Screening? A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies","volume":"51","author":"Cavalcante","year":"2022","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, W., Fu, Y.-L., and Zhu, D. (2023). ResNet and Its Application to Medical Image Processing: Research Progress and Challenges. Comput. Methods Programs Biomed., 240.","DOI":"10.1016\/j.cmpb.2023.107660"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/BF01622200","article-title":"Assessment of Fracture Risk and Its Application to Screening for Postmenopausal Osteoporosis: Synopsis of a WHO Report","volume":"4","author":"Kanis","year":"1994","journal-title":"Osteoporos. Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5624\/isd.2011.41.3.101","article-title":"Utility of the Computed Tomography Indices on Cone Beam Computed Tomography Images in the Diagnosis of Osteoporosis in Women","volume":"41","author":"Koh","year":"2011","journal-title":"Imaging Sci. Dent."},{"key":"ref_19","first-page":"68","article-title":"Pantomography in Assessment of the Osteoporosis Risk Group","volume":"102","author":"Klemetti","year":"1994","journal-title":"Scand. J. Dent. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep Learning in Medical Image Analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_21","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and <0.5MB Model Size. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1093\/bib\/bbx044","article-title":"Deep Learning for Healthcare: Review, Opportunities and Challenges","volume":"19","author":"Miotto","year":"2018","journal-title":"Brief. Bioinform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20220135","DOI":"10.1259\/dmfr.20220135","article-title":"Osteoporosis Screening Support System from Panoramic Radiographs Using Deep Learning by Convolutional Neural Network","volume":"51","author":"Nakamoto","year":"2022","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20160475","DOI":"10.1259\/dmfr.20160475","article-title":"Capability of CBCT to Identify Patients with Low Bone Mineral Density: A Systematic Review","volume":"46","author":"Guerra","year":"2017","journal-title":"Dentomaxillofacial Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1007\/s00784-019-03193-4","article-title":"A New Cone-Beam Computed Tomography\u2013Driven Index for Osteoporosis Prediction","volume":"24","author":"Carvalho","year":"2020","journal-title":"Clin. Oral Investig."}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/9\/5\/141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:55:52Z","timestamp":1760129752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/9\/5\/141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["tomography9050141"],"URL":"https:\/\/doi.org\/10.3390\/tomography9050141","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,22]]}}}