{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T17:37:22Z","timestamp":1785519442381,"version":"3.56.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T00:00:00Z","timestamp":1753488000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T00:00:00Z","timestamp":1753488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Delta University for Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Pediatric dental image analysis faces critical challenges in disease detection due to missing or corrupted pixel regions and the unique developmental characteristics of deciduous teeth, with current Latent Diffusion Models (LDMs) failing to preserve anatomical integrity during reconstruction of pediatric oral structures. We developed two novel biologically-inspired loss functions integrated within LDMs specifically designed for pediatric dental imaging: Gum-Adaptive Pixel Imputation (GAPI) utilizing adaptive 8-connected pixel neighborhoods that mimic pediatric gum tissue adaptive behavior, and Deciduous Transition-Based Reconstruction (DTBR) incorporating developmental stage awareness based on primary teeth transition patterns observed in children aged 2\u201312 years. These algorithms guide the diffusion process toward developmentally appropriate reconstructions through specialized loss functions that preserve structural continuity of deciduous dentition and age-specific anatomical features crucial for accurate pediatric diagnosis. Experimental validation on 2,255 pediatric dental images across six conditions (caries, calculus, gingivitis, tooth discoloration, ulcers, and hypodontia) demonstrated superior image generation performance with Inception Score of 9.87, Fr\u00e9chet Inception Distance of 4.21, Structural Similarity Index of 0.952, and Peak Signal-to-Noise Ratio of 34.76, significantly outperforming eleven competing diffusion models. Pediatric disease detection using enhanced datasets achieved statistically significant improvements across five detection models: +0.0694 in mean Average Precision [95% CI: 0.0608\u20130.0780], +\u20090.0606 in Precision [0.0523\u20130.0689], +\u20090.0736 in Recall [0.0651\u20130.0821], and +\u20090.0678 in F1-Score [0.0597\u20130.0759] (all <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001), enabling pediatric dentists to detect early-stage caries, developmental anomalies, and eruption disorders with unprecedented accuracy. This framework revolutionizes pediatric dental diagnosis by providing pediatric dentists with AI-enhanced imaging tools that account for the unique biological characteristics of developing dentition, significantly improving early detection of oral diseases in children and establishing a foundation for age-specific dental AI applications that enhance clinical decision-making in pediatric dental practice.<\/jats:p>","DOI":"10.1038\/s41598-025-11955-2","type":"journal-article","created":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T12:27:14Z","timestamp":1753532834000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["DentoMorph-LDMs: diffusion models based on novel adaptive 8-connected gum tissue and deciduous teeth loss for dental image augmentation"],"prefix":"10.1038","volume":"15","author":[{"given":"Hanaa Salem","family":"Marie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Elbaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riham sobhy","family":"Soliman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amira Abdelhafeez","family":"elkhatib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"issue":"10194","key":"11955_CR1","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/S0140-6736(19)31146-8","volume":"394","author":"MA Peres","year":"2019","unstructured":"Peres, M. A. et al. Oral diseases: a global public health challenge. Lancet 394 (10194), 249\u2013260 (2019).","journal-title":"Lancet"},{"issue":"3","key":"11955_CR2","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3390\/geriatrics6030076","volume":"6","author":"AKY Chan","year":"2021","unstructured":"Chan, A. K. Y. et al. Common medical and dental problems of older adults: a narrative review. Geriatrics 6 (3), 76 (2021).","journal-title":"Geriatrics"},{"key":"11955_CR3","unstructured":"Makhmirzaeva, G. G. et al. The importance of early screening in preventing chronic diseases. Revista Latinoam. De Hipertension, 1, (2025)."},{"issue":"1","key":"11955_CR4","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1186\/s40537-023-00817-1","volume":"10","author":"NA Baghdadi","year":"2023","unstructured":"Baghdadi, N. A. et al. Advanced machine learning techniques for cardiovascular disease early detection and diagnosis. J. Big Data. 10 (1), 144 (2023).","journal-title":"J. Big Data"},{"issue":"2","key":"11955_CR5","doi-asserted-by":"publisher","first-page":"3489","DOI":"10.32604\/cmc.2023.033356","volume":"74","author":"IM El-Henawy","year":"2023","unstructured":"El-Henawy, I. M., Elbaz, M., Ali, Z. H. & Sakr, N. Novel framework of segmentation 3D MRI of brain tumors. Comput. Mater. Contin. 74 (2), 3489\u20133502. https:\/\/doi.org\/10.32604\/cmc.2023.033356 (2023).","journal-title":"Comput. Mater. Contin"},{"issue":"2","key":"11955_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00784-025-06216-5","volume":"29","author":"F Sohrabniya","year":"2025","unstructured":"Sohrabniya, F. et al. Exploring a decade of deep learning in dentistry: A comprehensive mapping review. Clin. Oral Invest. 29 (2), 1\u201318 (2025).","journal-title":"Clin. Oral Invest."},{"issue":"2","key":"11955_CR7","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.32604\/cmc.2023.033111","volume":"74","author":"M Elbaz","year":"2023","unstructured":"Elbaz, M., Elwahsh, H. & El-Henawy, I. M. Proposed framework for detection of breast tumors. Comput. Mater. Contin. 74 (2), 2927\u20132944. https:\/\/doi.org\/10.32604\/cmc.2023.033111 (2023).","journal-title":"Comput. Mater. Contin"},{"issue":"21","key":"11955_CR8","doi-asserted-by":"publisher","first-page":"2442","DOI":"10.3390\/diagnostics14212442","volume":"14","author":"SS Alharbi","year":"2024","unstructured":"Alharbi, S. S. & Alhasson, H. F. Exploring the applications of artificial intelligence in dental image detection: A systematic review. Diagnostics 14 (21), 2442 (2024).","journal-title":"Diagnostics"},{"issue":"4","key":"11955_CR9","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1109\/JBHI.2021.3117575","volume":"26","author":"K Panetta","year":"2021","unstructured":"Panetta, K., Rajendran, R., Ramesh, A., Rao, S. P. & Agaian, S. Tufts dental database: a multimodal panoramic x-ray dataset for benchmarking diagnostic systems. IEEE J. Biomedical Health Inf. 26 (4), 1650\u20131659 (2021).","journal-title":"IEEE J. Biomedical Health Inf."},{"key":"11955_CR10","unstructured":"Asif, S. et al. Advancements and prospects of machine learning in medical diagnostics: unveiling the future of diagnostic precision. Archives Comput. Methods Engineering, 1\u201331, (2024)."},{"issue":"1","key":"11955_CR11","doi-asserted-by":"publisher","first-page":"5312","DOI":"10.1038\/s41598-023-30548-5","volume":"13","author":"R Khan","year":"2023","unstructured":"Khan, R. et al. Dental image enhancement network for early diagnosis of oral dental disease. Sci. Rep. 13 (1), 5312 (2023).","journal-title":"Sci. Rep."},{"issue":"11","key":"11955_CR12","doi-asserted-by":"publisher","first-page":"12561","DOI":"10.1007\/s10462-023-10453-z","volume":"56","author":"E Goceri","year":"2023","unstructured":"Goceri, E. Medical image data augmentation: techniques, comparisons and interpretations. Artif. Intell. Rev. 56 (11), 12561\u201312605 (2023).","journal-title":"Artif. Intell. Rev."},{"key":"11955_CR13","doi-asserted-by":"crossref","unstructured":"Khosla, C. & Saini, B. S. Enhancing performance of deep learning models with different data augmentation techniques: A survey, in international conference on intelligent engineering and management (ICIEM), IEEE, 79\u201385. (2020).","DOI":"10.1109\/ICIEM48762.2020.9160048"},{"key":"11955_CR14","unstructured":"Peng, M. et al. Diffusion models for intelligent transportation systems: A survey,  Preprint at https:\/\/arXiv.org\/abs\/2409.15816, (2024)."},{"issue":"4","key":"11955_CR15","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3390\/jimaging9040081","volume":"9","author":"A Kebaili","year":"2023","unstructured":"Kebaili, A., Lapuyade-Lahorgue, J. & Ruan, S. Deep learning approaches for data augmentation in medical imaging: a review. J. Imaging. 9 (4), 81 (2023).","journal-title":"J. Imaging"},{"key":"11955_CR16","doi-asserted-by":"publisher","unstructured":"Elbaz, M. et al. Novel framework for Detecting Multiple Sclerosis using Hybrid models, 32nd International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, 57\u201363, (2022). https:\/\/doi.org\/10.1109\/ICCTA58027.2022.10206298","DOI":"10.1109\/ICCTA58027.2022.10206298"},{"key":"11955_CR17","doi-asserted-by":"publisher","first-page":"118654","DOI":"10.1109\/ACCESS.2022.3220335","volume":"10","author":"SL Chen","year":"2022","unstructured":"Chen, S. L. et al. Missing teeth and restoration detection using dental panoramic radiography based on transfer learning with CNNs. IEEE Access. 10, 118654\u2013118664 (2022).","journal-title":"IEEE Access."},{"issue":"2","key":"11955_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s11282-023-00719-1","volume":"40","author":"S Yang","year":"2024","unstructured":"Yang, S., Kim, K. D., Ariji, E. & Kise, Y. Generative adversarial networks in dental imaging: a systematic review. Oral Radiol. 40 (2), 93\u2013108 (2024).","journal-title":"Oral Radiol."},{"key":"11955_CR19","doi-asserted-by":"publisher","first-page":"109241","DOI":"10.1016\/j.compbiomed.2024.109241","volume":"182","author":"AA Al-Haddad","year":"2024","unstructured":"Al-Haddad, A. A. et al. Towards dental diagnostic systems: synergizing wavelet transform with generative adversarial networks for enhanced image data fusion. Comput. Biol. Med. 182, 109241 (2024).","journal-title":"Comput. Biol. Med."},{"key":"11955_CR20","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models, in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 10684\u201310695. Accessed: Apr. 29, 2025. [Online]. (2022). Available: https:\/\/openaccess.thecvf.com\/content\/CVPR2022\/html\/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"11955_CR21","doi-asserted-by":"crossref","unstructured":"Takagi, Y. & Nishimoto, S. High-resolution image reconstruction with latent diffusion models from human brain activity, in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition,14453\u201314463. (2023).","DOI":"10.1109\/CVPR52729.2023.01389"},{"issue":"3","key":"11955_CR22","doi-asserted-by":"publisher","first-page":"e232471","DOI":"10.1148\/radiol.232471","volume":"312","author":"LR Koetzier","year":"2024","unstructured":"Koetzier, L. R. et al. Generating synthetic data for medical imaging. Radiology 312 (3), e232471 (2024).","journal-title":"Radiology"},{"key":"11955_CR23","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1007\/s11548-019-02019-8","volume":"14","author":"M Kugler","year":"2019","unstructured":"Kugler, M. et al. Robust 3D image reconstruction of pancreatic cancer tumors from histopathological images with different stains and its quantitative performance evaluation. Int. J. Comput. Assist. Radiol. Surg. 14, 2047\u20132055 (2019).","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"11955_CR24","doi-asserted-by":"publisher","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","volume":"88","author":"A Kazerouni","year":"2023","unstructured":"Kazerouni, A. et al. Diffusion models in medical imaging: A comprehensive survey. Med. Image. Anal. 88, 102846 (2023).","journal-title":"Med. Image. Anal."},{"key":"11955_CR25","doi-asserted-by":"publisher","first-page":"108510","DOI":"10.1016\/j.engappai.2024.108510","volume":"133","author":"W Brahmi","year":"2024","unstructured":"Brahmi, W., Jdey, I. & Drira, F. Exploring the role of convolutional neural networks (CNN) in dental radiography segmentation: a comprehensive systematic literature review. Eng. Appl. Artif. Intell. 133, 108510 (2024).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"11955_CR26","doi-asserted-by":"crossref","unstructured":"Anil, S., Sudeep, K., Saratchandran, S. & Sweety, V. K. Revolutionizing dental caries diagnosis through artificial intelligence, In: Dental Caries Perspectives-A Collection of Thoughtful Essays, IntechOpen, (2023).","DOI":"10.5772\/intechopen.112979"},{"key":"11955_CR27","doi-asserted-by":"crossref","unstructured":"AbuSalim, S. et al. Analysis of deep learning techniques for dental informatics: a systematic literature review, In:Healthcare, MDPI, 1892. (2022).","DOI":"10.3390\/healthcare10101892"},{"key":"11955_CR28","doi-asserted-by":"publisher","unstructured":"Ilesanmi, A. E., Ilesanmi, T., Idowu, O. P., Torigian, D. A. & Udupa, J. K. Organ segmentation from computed tomography images using the 3D convolutional neural network: a systematic review, Int J Multimed Info Retr,11, 315\u2013331, (2022). https:\/\/doi.org\/10.1007\/s13735-022-00242-9","DOI":"10.1007\/s13735-022-00242-9"},{"key":"11955_CR29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Pears, N., Rosin, P. L. & Huber, P. 3D Imaging, Analysis and Applications  11 (Springer, 2020).","DOI":"10.1007\/978-3-030-44070-1"},{"issue":"1","key":"11955_CR30","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1038\/s41598-024-83592-0","volume":"15","author":"AM Elshewey","year":"2025","unstructured":"Elshewey, A. M. et al. Enhancing heart disease classification based on Greylag Goose optimization algorithm and long short-term memory. Sci. Rep. 15 (1), 1277 (2025).","journal-title":"Sci. Rep."},{"key":"11955_CR31","doi-asserted-by":"publisher","first-page":"107417","DOI":"10.1016\/j.bspc.2024.107417","volume":"102","author":"Z Tarek","year":"2025","unstructured":"Tarek, Z., Alhussan, A. A., Khafaga, D. S., El-Kenawy, E. S. M. & Elshewey, A. M. A snake optimization algorithm-based feature selection framework for rapid detection of cardiovascular disease in its early stages. Biomed. Signal Process. Control. 102, 107417 (2025).","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"11955_CR32","doi-asserted-by":"publisher","first-page":"24489","DOI":"10.1038\/s41598-024-74475-5","volume":"14","author":"AM Elshewey","year":"2024","unstructured":"Elshewey, A. M., Alhussan, A. A., Khafaga, D. S., Elkenawy, E. S. M. & Tarek, Z. EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm. Sci. Rep. 14 (1), 24489 (2024).","journal-title":"Sci. Rep."},{"issue":"1","key":"11955_CR33","doi-asserted-by":"publisher","first-page":"23368","DOI":"10.1038\/s41598-024-73559-6","volume":"14","author":"AM Elshewey","year":"2024","unstructured":"Elshewey, A. M. & Osman, A. M. Orthopedic disease classification based on breadth-first search algorithm. Sci. Rep. 14 (1), 23368 (2024).","journal-title":"Sci. Rep."},{"issue":"2","key":"11955_CR34","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3390\/diagnostics15020154","volume":"15","author":"G Tasci","year":"2025","unstructured":"Tasci, G. et al. Zipper pattern: an investigation into psychotic criminal detection using EEG signals. Diagnostics 15 (2), 154 (2025).","journal-title":"Diagnostics"},{"key":"11955_CR35","doi-asserted-by":"publisher","first-page":"112555","DOI":"10.1016\/j.knosys.2024.112555","volume":"305","author":"T Tuncer","year":"2024","unstructured":"Tuncer, T. et al. Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification. Knowl. Based Syst. 305, 112555 (2024).","journal-title":"Knowl. Based Syst."},{"issue":"1","key":"11955_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-02355-7","volume":"15","author":"AM Elshewey","year":"2025","unstructured":"Elshewey, A. M., Selem, E. & Abed, A. H. Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS. Sci. Rep. 15 (1), 1\u201325 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"11955_CR37","doi-asserted-by":"publisher","first-page":"4409","DOI":"10.1038\/s41598-025-88459-6","volume":"15","author":"M Kaddes","year":"2025","unstructured":"Kaddes, M., Ayid, Y. M., Elshewey, A. M. & Fouad, Y. Breast cancer classification based on hybrid CNN with LSTM model. Sci. Rep. 15 (1), 4409 (2025).","journal-title":"Sci. Rep."},{"issue":"3","key":"11955_CR38","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.13005\/bpj\/1484","volume":"11","author":"B Goyal","year":"2018","unstructured":"Goyal, B., Agrawal, S. & Sohi, B. S. Noise issues prevailing in various types of medical images. Biomedical Pharmacol. J. 11 (3), 1227 (2018).","journal-title":"Biomedical Pharmacol. J."},{"key":"11955_CR39","doi-asserted-by":"publisher","first-page":"123796","DOI":"10.1016\/j.eswa.2024.123796","volume":"249","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Zhao, F., Liu, H. & Yu, J. Data and knowledge-driven deep multiview fusion network based on diffusion model for hyperspectral image classification. Expert Syst. Appl. 249, 123796 (2024).","journal-title":"Expert Syst. Appl."},{"key":"11955_CR40","unstructured":"Zhang, C., Zhang, C., Zhang, M. & Kweon, I. S. Text-to-image diffusion models in generative ai: A survey,  Preprint at https:\/\/arXiv.org\/abs\/2303.07909, (2023)."},{"key":"11955_CR41","doi-asserted-by":"crossref","unstructured":"Katwe, P. K., Khamparia, A., Gupta, D. & Dutta, A. K. Methodical systematic review of abstractive summarization and natural Language processing models for biomedical health informatics: approaches, metrics and challenges. ACM Trans. Asian Low-Resource Lang. Inform. Processing, (2023).","DOI":"10.1145\/3600230"},{"key":"11955_CR42","unstructured":"Sanborn, S. et al. Beyond euclid: An illustrated guide to modern machine learning with geometric, topological, and algebraic structures, Preprint at https:\/\/arXiv.org\/abs\/2407.09468, (2024)."},{"key":"11955_CR43","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.neucom.2022.04.127","volume":"497","author":"Y Tian","year":"2022","unstructured":"Tian, Y., Su, D., Lauria, S. & Liu, X. Recent advances on loss functions in deep learning for computer vision. Neurocomputing 497, 129\u2013158 (2022).","journal-title":"Neurocomputing"},{"key":"11955_CR44","unstructured":"Amos, B. Differentiable optimization-based modeling for machine learning, Ph. D. thesis, (2019)."},{"key":"11955_CR45","doi-asserted-by":"crossref","unstructured":"Rahman, O. et al. Design of novel loss functions for deep learning in x-ray CT, in 7th International Conference on Image Formation in X-Ray Computed Tomography, SPIE, 629\u2013634. (2022).","DOI":"10.1117\/12.2646473"},{"key":"11955_CR46","doi-asserted-by":"crossref","unstructured":"Wu, T., Liu, S., Zhang, H. & Zeng, T. Estimation-Denoising integration network architecture with updated parameter for MRI reconstruction. IEEE Trans. Comput. Imaging, (2025).","DOI":"10.1109\/TCI.2025.3531729"},{"key":"11955_CR47","unstructured":"Mohammadi, H. & Robustness of gradient methods for data driven decision making., University of Southern California, (2023)."},{"issue":"6","key":"11955_CR48","first-page":"1","volume":"42","author":"H Jiang","year":"2023","unstructured":"Jiang, H., Luo, A., Fan, H., Han, S. & Liu, S. Low-light image enhancement with wavelet-based diffusion models. ACM Trans. Graphics (TOG). 42 (6), 1\u201314 (2023).","journal-title":"ACM Trans. Graphics (TOG)"},{"issue":"3","key":"11955_CR49","doi-asserted-by":"publisher","first-page":"339","DOI":"10.3390\/plants14030339","volume":"14","author":"H Zhou","year":"2025","unstructured":"Zhou, H. et al. A novel Few-Shot learning framework based on diffusion models for High-Accuracy sunflower disease detection and classification. Plants 14 (3), 339 (2025).","journal-title":"Plants"},{"issue":"2","key":"11955_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3703632","volume":"21","author":"C Hu","year":"2024","unstructured":"Hu, C., Wei, X. Z. & Wu, X. J. DIRformer: A novel image restoration approach based on U-shaped transformer and diffusion models. ACM Trans. Multimedia Comput. Commun. Appl. 21 (2), 1\u201323 (2024).","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"11955_CR51","doi-asserted-by":"publisher","first-page":"9669","DOI":"10.1007\/s00521-025-11059-y","volume":"37","author":"HS Marie","year":"2025","unstructured":"Marie, H. S. & Elbaz, M. MCI-GAN: a novel GAN with identity blocks inspired by menstrual cycle behavior for missing pixel imputation. Neural Comput. Applic. 37, 9669\u20139703. https:\/\/doi.org\/10.1007\/s00521-025-11059-y (2025).","journal-title":"Neural Comput. Applic"},{"key":"11955_CR52","doi-asserted-by":"publisher","first-page":"23936","DOI":"10.1038\/s41598-024-73976-7","volume":"14","author":"GM Mahmoud","year":"2024","unstructured":"Mahmoud, G. M. et al. A novel 8-connected pixel identity GAN with neutrosophic (ECP-IGANN) for missing imputation. Sci. Rep. 14, 23936. https:\/\/doi.org\/10.1038\/s41598-024-73976-7 (2024).","journal-title":"Sci. Rep."},{"key":"11955_CR53","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1038\/s41598-024-82242-9","volume":"15","author":"GM Mahmoud","year":"2025","unstructured":"Mahmoud, G. M. et al. Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction. Sci. Rep. 15, 1102. https:\/\/doi.org\/10.1038\/s41598-024-82242-9 (2025).","journal-title":"Sci. Rep."},{"key":"11955_CR54","doi-asserted-by":"crossref","unstructured":"Deng, J. et al. Imagenet: A large-scale hierarchical image database, in IEEE conference on computer vision and pattern recognition, 248\u2013255 (2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11955_CR55","doi-asserted-by":"publisher","first-page":"16749","DOI":"10.1038\/s41598-025-99088-4","volume":"15","author":"GM Mahmoud","year":"2025","unstructured":"Mahmoud, G. M. et al. Menstrual cycle inspired latent diffusion model for image augmentation in energy production. Sci. Rep. 15, 16749. https:\/\/doi.org\/10.1038\/s41598-025-99088-4 (2025).","journal-title":"Sci. Rep."},{"issue":"1","key":"11955_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A. E. et al. MIMIC-III, a freely accessible critical care database. Sci. Data. 3 (1), 1\u20139 (2016).","journal-title":"Sci. Data"},{"key":"11955_CR57","doi-asserted-by":"publisher","first-page":"16635","DOI":"10.1038\/s41598-025-00600-7","volume":"15","author":"M Elbaz","year":"2025","unstructured":"Elbaz, M. et al. A dual GAN with identity blocks and pancreas-inspired loss for renewable energy optimization. Sci. Rep. 15, 16635. https:\/\/doi.org\/10.1038\/s41598-025-00600-7 (2025).","journal-title":"Sci. Rep."},{"issue":"4","key":"11955_CR58","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1002\/mp.13415","volume":"46","author":"Z Hu","year":"2019","unstructured":"Hu, Z. et al. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks. Med. Phys. 46 (4), 1686\u20131696 (2019).","journal-title":"Med. Phys."},{"issue":"5","key":"11955_CR59","doi-asserted-by":"publisher","first-page":"055024","DOI":"10.1088\/2057-1976\/abb068","volume":"6","author":"MA Hegazy","year":"2020","unstructured":"Hegazy, M. A., Cho, M. H. & Lee, S. Y. Image denoising by transfer learning of generative adversarial network for dental CT. Biomedical Phys. Eng. Express. 6 (5), 055024 (2020).","journal-title":"Biomedical Phys. Eng. Express"},{"key":"11955_CR60","doi-asserted-by":"publisher","first-page":"102028","DOI":"10.1016\/j.media.2021.102028","volume":"70","author":"Y Huang","year":"2021","unstructured":"Huang, Y. et al. Cephalogram synthesis and landmark detection in dental cone-beam CT systems. Med. Image. Anal. 70, 102028 (2021).","journal-title":"Med. Image. Anal."},{"key":"11955_CR61","doi-asserted-by":"publisher","first-page":"104139","DOI":"10.1016\/j.compbiomed.2020.104139","volume":"129","author":"MB Moran","year":"2021","unstructured":"Moran, M. B., Faria, M. D., Giraldi, G. A., Bastos, L. F. & Conci, A. Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs. Comput. Biol. Med. 129, 104139 (2021).","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"11955_CR62","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1177\/00220345221106676","volume":"101","author":"YS Park","year":"2022","unstructured":"Park, Y. S. et al. Deep learning\u2013based prediction of the 3D postorthodontic facial changes. J. Dent. Res. 101 (11), 1372\u20131379 (2022).","journal-title":"J. Dent. Res."},{"key":"11955_CR63","first-page":"191","volume":"15","author":"VK Krishnamoorthy","year":"2022","unstructured":"Krishnamoorthy, V. K. & Baskaran, S. Optimized adversarial network with faster residual deep learning for osteoarthritis classification in panoramic radiography. Int. J. Intell. Eng. Syst. 15, 191\u2013200 (2022).","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"11955_CR64","doi-asserted-by":"crossref","unstructured":"Shirsat, S. & Abraham, S. Crafting Synthetic Dental Radiographs with Generative Adversarial Networks, in IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), 1\u20136., IEEE, 1\u20136. (2024).","DOI":"10.1109\/SPICES62143.2024.10779891"},{"key":"11955_CR65","doi-asserted-by":"crossref","unstructured":"Wang, J., Zeng, J., Li, H. & Yu, X. A deep learning radiomics analysis for survival prediction in esophageal cancer, Journal of Healthcare Engineering,  1, 4034404,  (2022).","DOI":"10.1155\/2022\/4034404"},{"key":"11955_CR66","doi-asserted-by":"crossref","unstructured":"Wang, J. et al. Inversesr: 3d brain mri super-resolution using a latent diffusion model, In: International Conference on Medical Image Computing and Computer-Assisted Intervention 438\u2013447. (Springer, 2023).","DOI":"10.1007\/978-3-031-43999-5_42"},{"key":"11955_CR67","doi-asserted-by":"crossref","unstructured":"Grebe, A. V. & Wagman, M. Nucleon-Pion spectroscopy from sparsened correlators, Preprint at https:\/\/arXiv\/org\/abs\/2312.00321, (2023).","DOI":"10.2172\/2006478"},{"key":"11955_CR68","doi-asserted-by":"crossref","unstructured":"Ho, M. M., Dubey, S., Chong, Y., Knudsen, B. & Tasdizen, T. Latent diffusion models with histopathology Pre-Trained embeddings for unpaired frozen section to FFPE translation (F2FLDM), (2024).","DOI":"10.1109\/WACV61041.2025.00430"},{"key":"11955_CR69","doi-asserted-by":"crossref","unstructured":"Zhang, Z. et al. DiffBoost: enhancing medical image segmentation via Text-Guided diffusion model. IEEE Trans. Med. Imaging, (2024).","DOI":"10.1109\/TMI.2024.3519307"},{"issue":"1","key":"11955_CR70","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41598-023-27808-9","volume":"13","author":"A Qayyum","year":"2023","unstructured":"Qayyum, A. et al. Dental caries detection using a semi-supervised learning approach. Sci. Rep. 13 (1), 749 (2023).","journal-title":"Sci. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11955-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11955-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11955-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T01:36:50Z","timestamp":1757295410000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11955-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,26]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["11955"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-11955-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,26]]},"assertion":[{"value":"22 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"27268"}}