Incidence of lower eyelid asymmetry: an anthropometric analysis of 204 patients

Deep learning-based image analysis for automated measurement of eyelid morphology before and after blepharoptosis surgery

BACKGROUND AND AIM

Eyelid position and contour abnormality could lead to various diseases, such as blepharoptosis, which is a common eyelid disease. Accurate assessment of eyelid morphology is important in the management of blepharoptosis. We aimed to proposed a novel deep learning-based image analysis to automatically measure eyelid morphological properties before and after blepharoptosis surgery.

METHODS

This study included 135 ptotic eyes of 103 patients who underwent blepharoptosis surgery. Facial photographs were taken preoperatively and postoperatively. Margin reflex distance (MRD) 1 and 2 of the operated eyes were manually measured by a senior surgeon. Multiple eyelid morphological parameters, such as MRD1, MRD2, upper eyelid length and corneal area, were automatically measured by our deep learning-based image analysis. Agreement between manual and automated measurements, as well as two repeated automated measurements of MRDs were analysed. Preoperative and postoperative eyelid morphological parameters were compared. Postoperative eyelid contour symmetry was evaluated using multiple mid-pupil lid distances (MPLDs).

RESULTS

The intraclass correlation coefficients (ICCs) between manual and automated measurements of MRDs ranged from 0.934 to 0.971 (p < .001), and the bias ranged from 0.09 mm to 0.15 mm. The ICCs between two repeated automated measurements were up to 0.999 (p < .001), and the bias was no more than 0.002 mm. After surgery, MRD1 increased significantly from 0.31 ± 1.17 mm to 2.89 ± 1.06 mm, upper eyelid length from 19.94 ± 3.61 mm to 21.40 ± 2.40 mm, and corneal area from 52.72 ± 15.97 mm2 to 76.31 ± 11.31mm2 (all p < .001). Postoperative binocular MPLDs at different angles (from 0° to 180°) showed no significant differences in the patients.

CONCLUSION

This technique had high accuracy and repeatability for automatically measuring eyelid morphology, which allows objective assessment of blepharoptosis surgical outcomes. Using only patients' photographs, this technique has great potential in diagnosis and management of other eyelid-related diseases.

A Novel Automatic Morphologic Analysis of Eyelids Based on Deep Learning Methods

Purpose: To propose a deep-learning-based approach to automatically and objectively evaluate morphologic eyelid features using two-dimensional(2D) digital photographs and to assess the agreement between automatic and manual measurements.Materials and Methods: The 2D photographs of 1378 normal Asian participants (2756 eyes) were included for training, validating and testing the cornea and eyelid segmentation network. Margin reflex distance 1 (MRD1) and margin reflex distance 2 (MRD2) of 406 eyes from 203 participants were manually evaluated by 3 ophthalmologists and the photographs of 406 eyes were measured automatically for 8 morphologic eyelid features. The Spearman's correlation coefficient, intra-class correlation coefficient (ICC) and Bland-Altman analyses were used to determine the agreement between manual and automatic MRDs.Results: The dice coefficient was 0.922 and 0.974 for eyelid and cornea segmentation, respectively. A strong correlation was shown between manually and automatically measured MRD1 (r = 0.993, ICC = 0.996) and MRD2 (r = 0.950, ICC = 0.974). Bland-Altman analyses also showed excellent reliability with bias being 0.04 mmbetween automated and manual MRD1 measurements and 0.06 mm for MRD2. Automatically measured 8 features (MRD1, MRD2, palpebral fissure, medial area, lateral area, cornea area, upper and lower eyelid lengths) were found to be increased with age and peaked around the age range of 21 to 30 years.Conclusions: The proposed novel integrative analysis scheme was comparable with human performance. The approach with excellent reliability and reproductivity showed great potential for automated diagnosis and remote monitoring of eyelid-related diseases.

Eyelid Malposition after Blepharoplasty: An Ounce of Prevention

Upper and lower eyelid blepharoplasty are common procedures performed to provide a more youthful and rejuvenated appearance. However, this seemingly straightforward procedure may result in lid malpositions, frustrating the patient and surgeon alike, which ultimately require further treatment. We review preoperative assessment pearls to avoid these lid malpositions, as well as options for treating any postoperative complications related to lid position. Many of the techniques discussed in this article, in addition to many other oculoplastic procedures, are available to view in Dr. Richard C. Allen's operative video library at: http://webeye.ophth.uiowa.edu/eyeforum/video/plastics/ .

Topographic analysis of eyelid position using digital image processing software

PURPOSE

To propose a novel analysis technique for objective quantification of topographic eyelid position with an algorithmatically calculated scheme and to determine its feasibility.

METHODS

One hundred normal eyelids from 100 patients were segmented using a graph cut algorithm, and 11 shape features of eyelids were semi-automatically quantified using in-house software. To evaluate the intra- and inter-examiner reliability of this software, intra-class correlation coefficients (ICCs) were used. To evaluate the diagnostic value of this scheme, the correlations between semi-automatic and manual measurements of margin reflex distance 1 (MRD1) and margin reflex distance 2 (MRD2) were analysed using a Bland-Altman analysis. To determine the degree of agreement according to manual MRD length, the relationship between the variance of semi-automatic measurements and the manual measurements was evaluated using linear regression.

RESULTS

Intra- and inter-examiner reliability were excellent, with ICCs ranging from 0.913 to 0.980 in 11 shape features including MRD1, MRD2, palpebral fissure, lid perimeter, upper and lower lid lengths, roundness, total area, and medial, central, and lateral areas. The correlations between semi-automatic and manual MRDs were also excellent, with better correlation in MRD1 than in MRD2 (R = 0.893 and 0.823, respectively). In addition, significant positive relationships were observed between the variance and the length of MRD1 and 2; the longer the MRD length, the more the variance.

CONCLUSION

The proposed novel optimized integrative scheme, which is shown to have high repeatability and reproducibility, is useful for topographic analysis of eyelid position.