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ORIGINAL RESEARCH article

Front. Med.

Sec. Dermatology

A Multi-Model Fusion Approach incorporating Conventional Radiological and Machine Learning Features Across Age Spectrum for Periorbital fat Status Prediction

Provisionally accepted
Meng WangMeng Wang1,2Yudi HanYudi Han1Li LiLi Li1Xi LuXi Lu1,2Yiqing JiaYiqing Jia3Lingli GuoLingli Guo1*Yan HanYan Han1,4*
  • 11st Medical Center of Chinese PLA General Hospital, Beijing, China
  • 2Central Medical Branch of PLA General Hospital, Beijing, China
  • 36th Medical Center of Chinese PLA General Hospital,, Beijing, China
  • 4People's Liberation Army General Hospital, Beijing, China

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Objectives: To develop an ensemble learning model fusing conventional radiomics (CR) and machine learning (ML) features to assess periorbital fat status across the entire age spectrum. Methods: Retrospective analysis was conducted on preoperative cranial and facial MRI data of meningioma patients. Patients were categorized into youth, middle-aged, and senior groups and allocated to training and test sets through stratified random sampling. CR and ML features of fat in three periorbital regions were extracted to develop an ensemble learning model, with its clinical application value subsequently evaluated. Results: 237 patients were enrolled: 165 in the training set and 72 in the test set. The training set comprised 19 youth cases (28.5 ± 5.0, 7 male), 41 middle-aged cases (42.9 ± 4.7, 9 male), and 105 senior cases (60.0 ± 6.5, 26 male). The test set included 8 youth cases (28.6 ± 5.6, 4 male), 18 middle-aged cases (43.9 ± 4.1, 6 male), and 46 senior cases (58.8 ± 6.7, 10 male). The ensemble learning model outperformed the CR model, the ML model, and the CR-ML fusion model on the test set, achieving an AUC-macro of 0.833 (95% CI: 0.737–0.902), an F1-score of 0.614, an accuracy (Acc) of 0.597, and a positive predictive value (PPV) of 0.690. Ensemble learning models demonstrated optimal comprehensive capabilities in multi-classification tasks, enhancing generalization and robustness. Conclusion: Our ensemble learning model achieved non-invasive and reliable assessment of periorbital fat status across the entire age spectrum, enriching the evaluation methodology for rejuvenation surgery.

Keywords: machine learning, MRI, periorbital fat, Radiomics, Stacking ensemble learning

Received: 22 Nov 2025; Accepted: 10 Feb 2026.

Copyright: © 2026 Wang, Han, Li, Lu, Jia, Guo and Han. 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:
Lingli Guo
Yan Han

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material