{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:51:45Z","timestamp":1791827505380,"version":"4.3.4"},"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011512","name":"National Science Foundation, United Arab Emirates","doi-asserted-by":"publisher","award":["19300636"],"award-info":[{"award-number":["19300636"]}],"id":[{"id":"10.13039\/100011512","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Diabetic foot ulcers (DFUs) are serious complications of diabetes associated with delayed healing, infection, and limb amputation. Modelling DFU progression remains challenging because publicly available image datasets generally contain independent single-timepoint images rather than longitudinal follow-up sequences. This study proposes a texture-aware conditional diffusion framework for generating severity-conditioned, progression-like synthetic DFU images from a single RGB input. Instead of claiming patient-specific temporal prediction, the framework models plausible population-level visual transitions across ordinal pseudo-stages derived from clinically relevant severity cues. The full model achieved an SSIM of 0.851\u2009\u00b1\u20090.020, PSNR of 24.76\u2009\u00b1\u20091.12 dB, and FID of 44.33\u2009\u00b1\u20095.02, with higher observed SSIM and PSNR and lower FID values than the evaluated GAN and VAE baselines across structural fidelity and distributional realism. The GAN baseline achieved SSIM 0.782\u2009\u00b1\u20090.031, PSNR 21.98\u2009\u00b1\u20091.56 dB, and FID 59.13\u2009\u00b1\u20096.59, while the VAE baseline achieved SSIM 0.738\u2009\u00b1\u20090.038, PSNR 19.63\u2009\u00b1\u20091.83 dB, and FID 69.77\u2009\u00b1\u20097.44. The framework also obtained a Texture Consistency Score of 0.79\u2009\u00b1\u20090.04 and a Granulation Tissue Fidelity Score of 0.76\u2009\u00b1\u20090.05, indicating preservation of fine-grained texture and granulation-related appearance patterns. Ablation results confirmed the contribution of texture conditioning and cross-attention to image fidelity. Advanced severity-state analysis produced SSIM values of 0.674, 0.804, and 0.726 across three pseudo-stages, supporting severity-aligned structural consistency. Since true longitudinal future-state images were unavailable, these metrics indicate synthetic image fidelity and distributional realism, not longitudinal progression accuracy. Longitudinal DFU cohorts and larger clinician-centred validation remain necessary before clinical decision-support use or temporal progression claims can be justified. To assess generalization beyond the primary 493-image dataset, external validation was additionally performed on an independent DFU dataset under a zero-shot setting, demonstrating stable cross-dataset performance with only modest degradation in image fidelity and texture consistency. Therefore, the proposed framework should be interpreted as modelling cross-sectional severity-conditioned appearance interpolation across independent cases, rather than true patient-specific future progression prediction.<\/jats:p>","DOI":"10.1371\/journal.pone.0359540","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:30:50Z","timestamp":1791826250000},"page":"e0359540","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Texture-aware diffusion modelling for severity-conditioned synthetic progression-like visualization of diabetic foot ulcers from single images"],"prefix":"10.1371","volume":"21","author":[{"given":"Pratham","family":"Kaushik","sequence":"first","affiliation":[{"name":"Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University","place":["Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinay","family":"Kukreja","sequence":"additional","affiliation":[{"name":"Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University","place":["Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eshika","family":"Jain","sequence":"additional","affiliation":[{"name":"Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University","place":["Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9250-6225","authenticated-orcid":true,"given":"Modafar","family":"Ati","sequence":"additional","affiliation":[{"name":"Computer Science and Information Technology, College of Engineering, Abu Dhabi University","place":["Abu Dhabi, United Arab Emirates"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanmugasundaram","family":"Hariharan","sequence":"additional","affiliation":[{"name":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology","place":["Chennai, India"],"department":["Department of Computer Science and Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"issue":"4","key":"pone.0359540.ref001","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.jtv.2024.07.002","article-title":"Predictors of post-healing recurrence in patients with diabetic foot ulcers: A systematic review and meta-analysis","volume":"33","author":"Y Sun","year":"2024","journal-title":"J Tissue Viability"},{"issue":"10","key":"pone.0359540.ref002","first-page":"1961","article-title":"Elevated serum levels of Lp-PLA2 and IL-18 are associated with progression of diabetic foot ulcers","volume":"6","author":"T Chen","year":"2020","journal-title":"Clin Lab"},{"issue":"1","key":"pone.0359540.ref003","doi-asserted-by":"crossref","DOI":"10.1136\/bmjdrc-2019-001122","article-title":"Predicting the risk of future diabetic foot ulcer occurrence: A prospective cohort study of patients with diabetes in Tanzania","volume":"8","author":"R Naemi","year":"2020","journal-title":"BMJ Open Diabetes Res Care"},{"key":"pone.0359540.ref004","doi-asserted-by":"crossref","first-page":"105055","DOI":"10.1016\/j.compbiomed.2021.105055","article-title":"Diabetic foot ulcer classification using mapped binary patterns and convolutional neural networks","volume":"140","author":"N Al-Garaawi","year":"2022","journal-title":"Comput Biol Med"},{"issue":"18","key":"pone.0359540.ref005","doi-asserted-by":"crossref","first-page":"55199","DOI":"10.1007\/s11042-023-17710-x","article-title":"Diabetic foot ulcer classification of hybrid convolutional neural network on hyperspectral imaging","volume":"83","author":"T Arumuga Maria Devi","year":"2023","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"pone.0359540.ref006","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1177\/09287329241296417","article-title":"Diabetic foot ulcer classification assessment employing an improved machine learning algorithm","volume":"33","author":"RK Gudivaka","year":"2025","journal-title":"Technol Health Care"},{"key":"pone.0359540.ref007","doi-asserted-by":"crossref","unstructured":"Wang M, Timbol-Cuison L. Machine learning classification techniques for diabetic foot ulcers. In: 2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), 2022. 249\u201355. https:\/\/doi.org\/10.1109\/aiam57466.2022.00054","DOI":"10.1109\/AIAM57466.2022.00054"},{"key":"pone.0359540.ref008","doi-asserted-by":"crossref","first-page":"104042","DOI":"10.1016\/j.compbiomed.2020.104042","article-title":"Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers","volume":"126","author":"RB Kim","year":"2020","journal-title":"Comput Biol Med"},{"key":"pone.0359540.ref009","doi-asserted-by":"crossref","first-page":"103219","DOI":"10.1016\/j.infrared.2020.103219","article-title":"Computer aided detection of diabetic foot ulcer using asymmetry analysis of texture and temperature features","volume":"105","author":"J Saminathan","year":"2020","journal-title":"Infrared Phy Tech"},{"key":"pone.0359540.ref010","doi-asserted-by":"crossref","unstructured":"Jain E, Rathour A. EfficientNet-based deep learning model for accurate diabetic foot ulcer classification. 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Pretrained deep learning networks for Diabetic Foot Ulcer (DFU) classification. In: 2025 24th International symposium INFOTEH-JAHORINA (INFOTEH). 2025. 1\u20136. https:\/\/doi.org\/10.1109\/infoteh64129.2025.10959253","DOI":"10.1109\/INFOTEH64129.2025.10959253"},{"key":"pone.0359540.ref014","doi-asserted-by":"crossref","first-page":"15655","DOI":"10.1007\/s11042-019-07820-w","article-title":"DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network","volume":"79","author":"L Alzubaidi","year":"2020","journal-title":"Multimed Tools Appl"},{"key":"pone.0359540.ref015","doi-asserted-by":"crossref","first-page":"103616","DOI":"10.1016\/j.compbiomed.2020.103616","article-title":"Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques","volume":"117","author":"M Goyal","year":"2020","journal-title":"Comput Biol Med"},{"key":"pone.0359540.ref016","doi-asserted-by":"crossref","unstructured":"Santos E, Santos F, Dallyson J, Aires K, Tavares JMRS, Veras R. Diabetic foot ulcers classification using a fine-tuned CNNs ensemble. In: 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022. 282\u20137. https:\/\/doi.org\/10.1109\/cbms55023.2022.00056","DOI":"10.1109\/CBMS55023.2022.00056"},{"key":"pone.0359540.ref017","doi-asserted-by":"crossref","first-page":"92776","DOI":"10.1109\/ACCESS.2021.3093358","article-title":"Automatic diabetic foot prediction through fundus images by radiomics features","volume":"9","author":"X Liang","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0359540.ref018","doi-asserted-by":"crossref","first-page":"98315","DOI":"10.1109\/ACCESS.2023.3312531","article-title":"DFU-SIAM a novel diabetic foot ulcer classification with deep learning","volume":"11","author":"MSA Toofanee","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0359540.ref019","doi-asserted-by":"crossref","unstructured":"Sengottaiyan N, Eswaran M, Sungheetha A, Sharma R, Pravin A, Ghantasala GP. Detection and classification of diabetic foot ulcers via cloud-based deep learning approach. In: 2024 3rd Ed. IEEE Delhi Sect. Flagsh. Conf. 2024. 1\u20136. https:\/\/doi.org\/10.1109\/DELCON64804.2024.10867128","DOI":"10.1109\/DELCON64804.2024.10867128"},{"key":"pone.0359540.ref020","doi-asserted-by":"crossref","first-page":"1543192","DOI":"10.3389\/fendo.2025.1543192","article-title":"Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot","volume":"16","author":"G-X Zhou","year":"2025","journal-title":"Front Endocrinol (Lausanne)"},{"key":"pone.0359540.ref021","doi-asserted-by":"crossref","first-page":"945020","DOI":"10.3389\/fendo.2022.945020","article-title":"A comprehensive review of methods based on deep learning for diabetes-related foot ulcers","volume":"13","author":"J Zhang","year":"2022","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"1","key":"pone.0359540.ref022","first-page":"2022894","article-title":"Mortality prediction model in patients with diabetic foot ulcer: A case-control study from a tertiary referral hospital in 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