Dynamic Eyelid Evaluation Using a Deep Neural Network in Upper Blepharoplasty: A Prospective Multicenter Pilot Study

Upper blepharoplasty is the most common cosmetic procedure in East Asia. A natural Asian double eyelid features specific crease characteristics. AI advancements, such as UNet and PointRend, enhance medical image segmentation, aiding in post-blepharoplasty evaluation. This study applies deep neural networks to analyze facial images, providing morphological parameters to assist surgeons in assessing outcomes and planning revisions.

This study included 102 eyes from 51 patients seeking for revisional blepharoplasty and 100 eyes from 50 volunteers with inborn double eyelid. Standardized images and videos were collected. The deep learning-based image analysis automatically evaluated four eyelid morphological parameters, including pre-tarsal show, corneal visibility ratio, dynamic value, and crease depth. Analysis was done on the agreement between the automated measures and the manual measurements. The parameters of the patients' and volunteers' eyelids were compared. FACE-Q surveys were used to measure patient-reported esthetic outcomes.

The intraclass correlation coefficients between manual measures and automated measurements of pre-tarsal show, corneal visibility ratio, and dynamic value were 0.973, 0.975, and 0.965. At the long-term follow-up, the pre-tarsal show and crease depth decreased significantly, whereas the corneal visibility ratio and dynamic value increased significantly. FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (87.6) and were negatively correlated with pre-tarsal show (r = - 0.814, p = 0.000).

The deep neural network technique automatically measured the eyelid morphology with excellent precision and reproducibility, enabling an objective evaluation of the surgical outcomes for blepharoplasty.

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