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

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          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 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.

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          Most cited references41

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          The Measurement of Observer Agreement for Categorical Data

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            Deep learning-enabled medical computer vision

            A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.
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              A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: a review

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                Author and article information

                Journal
                Quant Imaging Med Surg
                Quant Imaging Med Surg
                QIMS
                Quantitative Imaging in Medicine and Surgery
                AME Publishing Company
                2223-4292
                2223-4306
                03 January 2023
                01 March 2023
                : 13
                : 3
                : 1592-1604
                Affiliations
                [1 ]Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University , deptSchool of Medicine , Hangzhou, China;
                [2 ]deptSchool of Electronic Engineering and Computer Science , Queen Mary University of London , London, UK;
                [3 ]deptCollege of Media Engineering , Communication University of Zhejiang , Hangzhou, China
                Author notes

                Contributions: (I) Conception and design: J Ye, L Lou, J Shao, X Huang; (II) Administrative support: J Ye; (III) Provision of study materials or patients: J Ye, T Gao; (IV) Collection and assembly of data: J Shao, X Huang, J Cao; (V) Data analysis and interpretation: J Shao, X Huang, Q Zhang, Y Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                [#]

                These authors contributed equally to this work.

                Correspondence to: Juan Ye; Lixia Lou. Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, No. 88 Jiefang Road, Hangzhou 310009, China. Email: yejuan@ 123456zju.edu.cn ; loulixia110@ 123456zju.edu.cn .
                Article
                qims-13-03-1592
                10.21037/qims-22-551
                10006102
                36915314
                7bd16912-799a-49d2-8cd6-46362a0c0029
                2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.

                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.

                History
                : 03 June 2022
                : 25 November 2022
                Categories
                Original Article

                thyroid-associated ophthalmopathy (tao),eyelid morphology,automatic measurement,facial images,deep learning

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