{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:21:23Z","timestamp":1785410483091,"version":"3.56.0"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271019"],"award-info":[{"award-number":["82271019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472149"],"award-info":[{"award-number":["82472149"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82471024"],"award-info":[{"award-number":["82471024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["82271019"],"award-info":[{"award-number":["82271019"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82472149"],"award-info":[{"award-number":["82472149"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["82471024"],"award-info":[{"award-number":["82471024"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["24ZDYF0099"],"award-info":[{"award-number":["24ZDYF0099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fr\u00e9chet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source.<\/jats:p>","DOI":"10.3390\/jcm15093344","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:55:39Z","timestamp":1777370139000},"page":"3344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["High-Fidelity Synthesis of Temporomandibular Joint Cone-Beam Computed Tomography Images via Latent Diffusion Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Qinlanhui","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Oral Diseases, National Centre for Stomatology and National Clinical Research Centre for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 600041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4906-2830","authenticated-orcid":false,"given":"Yunhao","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oral Diseases, National Centre for Stomatology and National Clinical Research Centre for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 600041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9040-6295","authenticated-orcid":false,"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Oral Diseases, National Centre for Stomatology and National Clinical Research Centre for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 600041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"h1154","DOI":"10.1136\/bmj.h1154","article-title":"Temporomandibular Disorders","volume":"350","author":"Durham","year":"2015","journal-title":"BMJ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s00784-020-03710-w","article-title":"Prevalence of Temporomandibular Joint Disorders: A Systematic Review and Meta-Analysis","volume":"25","author":"Valesan","year":"2021","journal-title":"Clin. Oral Investig."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1002\/ejp.2314","article-title":"Temporomandibular Disorder Pain Is Associated with Increased Sick Leave and Reduced Health Related Quality of Life","volume":"28","author":"Vallin","year":"2024","journal-title":"Eur. J. Pain"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"L\u00f6vgren, A., Liv, P., Durham, J., Goncalves, D.a.G., Kapos, F.P., Kothari, S.F., Drangsholt, M., Peck, C.C., Visscher, C.M., and Ong, L. (2025). Disability Weights for Global Burden Estimation of Orofacial Pain. J. Dent. Res., 220345251363852.","DOI":"10.1177\/00220345251363852"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.11607\/jop.1151","article-title":"Diagnostic Criteria for Temporomandibular Disorders (DC\/TMD) for Clinical and Research Applications: Recommendations of the International RDC\/TMD Consortium Network* and Orofacial Pain Special Interest Group\u2020","volume":"28","author":"Schiffman","year":"2014","journal-title":"J. Oral Facial Pain Headache"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20140235","DOI":"10.1259\/dmfr.20140235","article-title":"Temporomandibular Joint Diagnostics Using CBCT","volume":"44","author":"Larheim","year":"2015","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bianchi, J., Roberto Gon\u00e7alves, J., Carlos de Oliveira Ruellas, A., Vieira Pastana Bianchi, J., Ashman, L.M., Yatabe, M., Benavides, E., Soki, F.N., and Cevidanes, L.H.S. (2021). Radiographic Interpretation Using High-Resolution Cbct to Diagnose Degenerative Temporomandibular Joint Disease. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0255937"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1111\/joor.13386","article-title":"Diagnostic Performance of Magnetic Resonance Imaging for Degenerative Temporomandibular Joint Disease","volume":"50","author":"Liu","year":"2023","journal-title":"J. Oral Rehabil."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, L., Tao, K., Ma, J., Pan, X., Zhang, K., and Feng, J. (2025). Relationship between Temporomandibular Joint Space and Articular Disc Displacement. BMC Oral Health, 25.","DOI":"10.1186\/s12903-025-05991-7"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1111\/joor.13701","article-title":"Deep Learning for Temporomandibular Joint Arthropathies: A Systematic Review and Meta-Analysis","volume":"51","author":"Rokhshad","year":"2024","journal-title":"J. Oral Rehabil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A Guide to Deep Learning in Healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1038\/s41597-025-05398-7","article-title":"MMDental\u2014A Multimodal Dataset of Tooth CBCT Images with Expert Medical Records","volume":"12","author":"Wang","year":"2025","journal-title":"Sci. Data"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cui, W., Wang, Y., Zhang, Q., Zhou, H., Song, D., Zuo, X., Jia, G., and Zeng, L. (2022). CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images. arXiv.","DOI":"10.1007\/978-3-031-13841-6_18"},{"key":"ref_14","unstructured":"Tschuchnig, M.E., Steininger, P., and Gadermayr, M. (2024). CBCTLiTS: A Synthetic, Paired CBCT\/CT Dataset For Segmentation And Style Transfer. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s13012-024-01357-9","article-title":"Generative AI in Healthcare: An Implementation Science Informed Translational Path on Application, Integration and Governance","volume":"19","author":"Reddy","year":"2024","journal-title":"Implement. Sci."},{"key":"ref_16","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv."},{"key":"ref_17","unstructured":"Kingma, D.P., and Welling, M. (2022). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102799","DOI":"10.1016\/j.media.2023.102799","article-title":"ImUnity: A Generalizable VAE-GAN Solution for Multicenter MR Image Harmonization","volume":"88","author":"Cackowski","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thanh-Tung, H., and Tran, T. (2020). On Catastrophic Forgetting and Mode Collapse in Generative Adversarial Networks. arXiv.","DOI":"10.1109\/IJCNN48605.2020.9207181"},{"key":"ref_20","unstructured":"van den Oord, A., Vinyals, O., and Kavukcuoglu, K. (2018). Neural Discrete Representation Learning. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. arXiv.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"bpae062","DOI":"10.1093\/biomethods\/bpae062","article-title":"Advanced Image Generation for Cancer Using Diffusion Models","volume":"9","author":"Kidder","year":"2024","journal-title":"Biol. Methods Protoc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1109\/TMI.2025.3557430","article-title":"Towards Better Cephalometric Landmark Detection With Diffusion Data Generation","volume":"44","author":"Guo","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e210315","DOI":"10.1148\/ryai.210315","article-title":"RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning","volume":"4","author":"Mei","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_26","unstructured":"Xu, K., Li, C., Zhu, J., and Zhang, B. (2020). Understanding and Stabilizing GANs\u2019 Training Dynamics with Control Theory. arXiv."},{"key":"ref_27","unstructured":"Bredell, G., Flouris, K., Chaitanya, K., Erdil, E., and Konukoglu, E. (2023). Explicitly Minimizing the Blur Error of Variational Autoencoders. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104475","DOI":"10.1016\/j.jdent.2023.104475","article-title":"Deep Learning for Automated Segmentation of the Temporomandibular Joint","volume":"132","author":"Vinayahalingam","year":"2023","journal-title":"J. Dent."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105592","DOI":"10.1016\/j.jdent.2025.105592","article-title":"Automated Diagnosis and Classification of Temporomandibular Joint Degenerative Disease via Artificial Intelligence Using CBCT Imaging","volume":"154","author":"Mao","year":"2025","journal-title":"J. Dent."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106348","DOI":"10.1016\/j.jdent.2026.106348","article-title":"An Artificial Intelligence-Assisted Automated Quantitative Evaluation System for Condylar Morphological Changes of Temporomandibular Joint","volume":"166","author":"Li","year":"2026","journal-title":"J. Dent."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1111\/joor.13385","article-title":"Risk Estimation of Degenerative Joint Disease in Temporomandibular Disorder Patients with Different Types of Sagittal and Coronal Disc Displacements: MRI and CBCT Analysis","volume":"50","author":"Fan","year":"2023","journal-title":"J. Oral Rehabil."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1007\/s10791-026-09980-4","article-title":"An Efficient Approach to Synthesize Medical Images for Data Augmentation with Constrained Training Data","volume":"29","author":"Sharma","year":"2026","journal-title":"Discov. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, C., Kitamura, Y., Kudo, A., Ichinose, A., Rundo, L., Furukawa, Y., Umemoto, K., Li, Y., and Nakayama, H. (2019). Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-Based CT Image Augmentation for Object Detection, IEEE.","DOI":"10.1109\/3DV.2019.00085"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., Tudosiu, P.-D., Dafflon, J., da Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., and Cardoso, M.J. (2022). Brain Imaging Generation with Latent Diffusion Models. arXiv.","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102789","DOI":"10.1016\/j.media.2023.102789","article-title":"ADNI SynthSeg: Segmentation of Brain MRI Scans of Any Contrast and Resolution without Retraining","volume":"86","author":"Billot","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","article-title":"Diffusion Models in Medical Imaging: A Comprehensive Survey","volume":"88","author":"Kazerouni","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40510-026-00606-5","article-title":"CBCT Assisted Diagnosis System for Temporomandibular Joint Disc Displacement Based on Deep Learning","volume":"27","author":"Fu","year":"2026","journal-title":"Prog. Orthod."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/9\/3344\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T10:32:40Z","timestamp":1777372360000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/9\/3344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jcm15093344"],"URL":"https:\/\/doi.org\/10.3390\/jcm15093344","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]}}}