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ORIGINAL RESEARCH article
Front. Med.
Sec. Ophthalmology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1532195
This article is part of the Research Topic Efficient Artificial Intelligence in Ophthalmic Imaging – Volume II View all articles
Hierarchical Attention Transformer Provides Assistant Suggestions for Orbital Rejuvenation Surgery
Provisionally accepted- 1 Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- 2 University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
- 3 Institut Photovoltaïque d'Île-de-France, ParisTech École Nationale Supérieure de Chimie de Paris, Paris, France
Background: Early detection of periocular aging is a common concern in cosmetic surgery.Traditional diagnostic and treatment methods often require hospital visits and consultations with plastic surgeons, which are costly and time-consuming. This study aims to develop and evaluate an AI-based decision-making system for periocular cosmetic surgery, utilizing a Hierarchical Attention Transformer (HATrans) model designed for multi-label classification in periocular conditions, allowing for home-based early aging identification.
Keywords: periocular aging, hierarchical attention transformer (HATrans), AI-based Decisionmaking, multi-label classification, Lower blepharoplasty, double eyelid surgery, epicanthal fold surgery, lateral canthoplast
Received: 22 Nov 2024; Accepted: 19 Feb 2025.
Copyright: © 2025 Liu, Ke, Lian, Hu, Li, Wu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Kai Liu, Department of Plastic and Reconstructive Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
Qin Ke, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan Province, China
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