Cargando…

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

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 ophth...

Descripción completa

Detalles Bibliográficos
Autores principales: Shao, Ji, Huang, Xingru, Gao, Tao, Cao, Jing, Wang, Yaqi, Zhang, Qianni, Lou, Lixia, Ye, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006102/
https://www.ncbi.nlm.nih.gov/pubmed/36915314
http://dx.doi.org/10.21037/qims-22-551
_version_ 1784905238183936000
author Shao, Ji
Huang, Xingru
Gao, Tao
Cao, Jing
Wang, Yaqi
Zhang, Qianni
Lou, Lixia
Ye, Juan
author_facet Shao, Ji
Huang, Xingru
Gao, Tao
Cao, Jing
Wang, Yaqi
Zhang, Qianni
Lou, Lixia
Ye, Juan
author_sort Shao, Ji
collection PubMed
description 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 mm(2); 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.
format Online
Article
Text
id pubmed-10006102
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-100061022023-03-12 Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy Shao, Ji Huang, Xingru Gao, Tao Cao, Jing Wang, Yaqi Zhang, Qianni Lou, Lixia Ye, Juan Quant Imaging Med Surg Original Article 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 mm(2); 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. AME Publishing Company 2023-01-03 2023-03-01 /pmc/articles/PMC10006102/ /pubmed/36915314 http://dx.doi.org/10.21037/qims-22-551 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Shao, Ji
Huang, Xingru
Gao, Tao
Cao, Jing
Wang, Yaqi
Zhang, Qianni
Lou, Lixia
Ye, Juan
Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
title Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
title_full Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
title_fullStr Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
title_full_unstemmed Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
title_short Deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
title_sort deep learning-based image analysis of eyelid morphology in thyroid-associated ophthalmopathy
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006102/
https://www.ncbi.nlm.nih.gov/pubmed/36915314
http://dx.doi.org/10.21037/qims-22-551
work_keys_str_mv AT shaoji deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT huangxingru deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT gaotao deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT caojing deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT wangyaqi deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT zhangqianni deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT loulixia deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy
AT yejuan deeplearningbasedimageanalysisofeyelidmorphologyinthyroidassociatedophthalmopathy