{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T14:35:45Z","timestamp":1787668545484,"version":"build-2736575974"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study aims to develop a diffusion-based workflow to precisely predict postoperative appearance in blepharoptosis patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We developed PtosisDiffusion, a training-free workflow that combines face mesh with ControlNet for accurate post-operative predictions, and evaluated it using 39 preoperative photos from blepharoptosis patients. The performance of PtosisDiffusion was compared against three other diffusion-based methods: Conditional Diffusion, Repaint, and Dragon Diffusion.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>PtosisDiffusion demonstrated superior performance in subjective evaluations, including overall rating, correction, and double eyelid formation. Statistical analyses confirmed that PtosisDiffusion achieved the highest overlap ratio (0.87 <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"m1\"><mml:mrow><mml:mo>\u00b1<\/mml:mo><\/mml:mrow><\/mml:math><\/jats:inline-formula> 0.07) and an MPLPD ratio close to 1 (1.01 <jats:inline-formula><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"m2\"><mml:mrow><mml:mo>\u00b1<\/mml:mo><\/mml:mrow><\/mml:math><\/jats:inline-formula> 0.10). The model also showed robustness in extreme cases, and ablation studies confirmed the necessity of each model component.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>PtosisDiffusion generates accurate postoperative appearance predictions for ptosis patients using only preoperative photographs. Among the four models tested, PtosisDiffusion consistently outperformed the others in both subjective and statistical evaluation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcell.2024.1459336","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T13:22:47Z","timestamp":1730294567000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["PtosisDiffusion: a training-free workflow for precisely predicting post-operative appearance in blepharoptosis patients based on diffusion models"],"prefix":"10.3389","volume":"12","author":[{"given":"Shenyu","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"B1","first-page":"11341","volume-title":"Real-time pupil tracking from monocular video for digital puppetry","author":"Ablavatski","year":"2020"},{"key":"B2","doi-asserted-by":"publisher","first-page":"101684","DOI":"10.1016\/j.compmedimag.2019.101684","article-title":"Medgan: medical image translation using gans","volume":"79","author":"Armanious","year":"2020","journal-title":"Comput. 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