Deep Learning-based Assessment of Eyelid and Periorbital Parameters: Assisting Diagnosis and Treatment Planning in...

Blepharoptosis is a common eyelid disorder that impairs both vision and appearance, requiring accurate assessment for effective treatment. This study aimed to develop and evaluate a deep learning (DL)-based system for automatic measurement of eyelid and periorbital parameters and for classifying levator function (LF) in patients with blepharoptosis. We retrospectively included 1,177 patients who underwent ptosis surgery at a tertiary oculoplastic center from January 2016 to November 2021. LF was categorized into good (> 10 mm), fair (4-10 mm), and poor (≤ 4 mm) based on clinical evaluation. The DL model segmented eyelid and eyebrow regions and measured key parameters; manual measurements were performed for comparison. A multinomial logistic regression model incorporating DL-derived features and demographic data was used to predict LF grades. The DL system achieved high segmentation performance (Dice coefficient = 0.910) and strong agreement with manual measurements (ICC = 0.988 for MRD1; 0.902 for CBH). The regression model classified LF grades with an overall accuracy of 0.760 and an AUC of 0.829, within the range of ophthalmologist assessments (highest clinician accuracy = 0.767). This DL-based system offers an efficient, objective tool for periorbital assessment and LF grading, supporting personalized diagnosis and surgical planning in blepharoptosis management.