Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Ji Shao; Xingru Huang; Tao Gao; Jing Cao; Yaqi Wang; Qianni Zhang; Lixia Lou; Juan Ye

doi:10.21037/qims-22-551

Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy

Quant Imaging Med Surg. 2023 Mar 1;13(3):1592-1604. doi: 10.21037/qims-22-551. Epub 2023 Jan 3.

Authors

Ji Shao^#¹, Xingru Huang^#², Tao Gao¹, Jing Cao¹, Yaqi Wang³, Qianni Zhang², Lixia Lou¹, Juan Ye¹

Affiliations

¹ Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.
² School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
³ College of Media Engineering, Communication University of Zhejiang, Hangzhou, China.

^# Contributed equally.

Abstract

Background: We aimed to propose a deep learning-based approach to automatically measure eyelid morphology in patients with thyroid-associated ophthalmopathy (TAO).

Methods: This prospective study consecutively included 74 eyes of patients with TAO and 74 eyes of healthy volunteers visiting the ophthalmology department in a tertiary hospital. Patients diagnosed as TAO and healthy volunteers who were age- and gender-matched met the eligibility criteria for recruitment. Facial images were taken under the same light conditions. Comprehensive eyelid morphological parameters, such as palpebral fissure (PF) length, margin reflex distance (MRD), eyelid retraction distance, eyelid length, scleral area, and mid-pupil lid distance (MPLD), were automatically calculated using our deep learning-based analysis system. MRD1 and 2 were manually measured. Bland-Altman plots and intraclass correlation coefficients (ICCs) were performed to assess the agreement between automatic and manual measurements of MRDs. The asymmetry of the eyelid contour was analyzed using the temporal: nasal ratio of the MPLD. All eyelid features were compared between TAO eyes and control eyes using the independent samples t-test.

Results: A strong agreement between automatic and manual measurement was indicated. Biases of MRDs in TAO eyes and control eyes ranged from -0.01 mm [95% limits of agreement (LoA): -0.64 to 0.63 mm] to 0.09 mm (LoA: -0.46 to 0.63 mm). ICCs ranged from 0.932 to 0.980 (P<0.001). Eyelid features were significantly different in TAO eyes and control eyes, including MRD1 (4.82±1.59 vs. 2.99±0.81 mm; P<0.001), MRD2 (5.89±1.16 vs. 5.47±0.73 mm; P=0.009), upper eyelid length (UEL) (27.73±4.49 vs. 25.42±4.35 mm; P=0.002), lower eyelid length (LEL) (31.51±4.59 vs. 26.34±4.72 mm; P<0.001), and total scleral area (SA_TOTAL) (96.14±34.38 vs. 56.91±14.97 mm²; P<0.001). The MPLDs at all angles showed significant differences in the 2 groups of eyes (P=0.008 at temporal 180°; P<0.001 at other angles). The greatest temporal-nasal asymmetry appeared at 75° apart from the midline in TAO eyes.

Conclusions: Our proposed system allowed automatic, comprehensive, and objective measurement of eyelid morphology by only using facial images, which has potential application prospects in TAO. Future work with a large sample of patients that contains different TAO subsets is warranted.

Keywords: Thyroid-associated ophthalmopathy (TAO); automatic measurement; deep learning; eyelid morphology; facial images.