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

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

Ji Shao #  1 , Xingru Huang #  2 , Tao Gao  1 , Jing Cao  1 , Yaqi Wang  3 , Qianni Zhang  2 , Lixia Lou  1 , Juan Ye  1
Affiliations

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

Ji Shao et al. Quant Imaging Med Surg. .

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 (SATOTAL) (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.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-551/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Flowchart of the study population. TAO, thyroid-associated ophthalmopathy.
Figure 2
Figure 2
Workflow of the automatic eyelid analysis system. Recurrent residual convolutional neural networks with an attention gate connection based on U-Net (Attention R2U-Net) were used in the first stage for the detection of the eye and in the second stage for the segmentation of the eyelid and cornea contour. After the circle marker that was attached to the forehead of participants was located for pixel calculation, the eyelid parameters were transformed into the actual distances. This image has been published with the participant’s consent. MRD, margin reflex distance.
Figure 3
Figure 3
The architecture of the recurrent residual convolutional neural networks with an attention gate connection based on U-Net (Attention R2U-Net). Conv., convolution; ReLU, rectified linear unit; RRCU, recurrent residual convolutional unit; H, height; W, weight; C, channel; AG, attention gate.
Figure 4
Figure 4
A schematic diagram of the eyelid morphological parameters. MRD1 and 2 refer to the vertical distances from the pupil center to the upper and lower eyelids, respectively. PF is the sum of MRD1 and MRD2. The upper and lower eyelids were separated according to the inner and outer canthus. The scleral area was divided into 4 parts centered on the pupil. A radial line was drawn every 15° to calculate mid-pupil lid distances. MRD, margin reflex distance; PF, palpebral fissure.
Figure 5
Figure 5
Representative results of the automatic eyelid and cornea segmentation based on deep learning. (A,B) The original images of a TAO patient and an age- and gender-matched healthy volunteer. (C,D) The automatically segmented images of the eyelid and cornea. (C,D) The cornea is marked red, and the scleral is marked green. This has been published with the participants’ consent. TAO, thyroid-associated ophthalmopathy.
Figure 6
Figure 6
Bland-Altman plots demonstrating excellent agreement between automatic and manual measurements in MRD1 and MRD2. (A) The difference of MRD1 in TAO eyes. (B) The difference of MRD2 in TAO eyes. (C) The difference of MRD1 in control eyes. (D) The difference of MRD2 in control eyes. TAO, thyroid-associated ophthalmopathy; MRD, margin reflex distance; SD, standard deviation.
Figure 7
Figure 7
Comparison of the eyelid contour and symmetry in TAO eyes and control eyes. (A) A polar plot showing the eyelid contour of TAO eyes and control eyes according to MPLD. (B) A bar graph displaying the eyelid symmetry of TAO eyes and control eyes according to the temporal: nasal ratio of MPLDs. *, P<0.05; ***, P<0.001. TAO, thyroid-associated ophthalmopathy; MPLD, mid-pupil lid distance.
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