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Facial expression of patients with Graves’ orbitopathy

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Abstract

Purpose

Patients with Graves’ orbitopathy (GO) have characteristic facial expressions that are different from those of healthy individuals due to the combination of somatic and psychiatric symptoms. However, the facial expressions of GO patients have not yet been described and analyzed systematically. Thus, the present study aimed to present the facial expressions of GO patients and explore their applications in clinical practice.

Methods

Facial image and clinical data of 943 GO patients were included, and 126 patients answered quality of life (GO-QOL) questionnaires. Each patient was labeled for one facial expression. Then, a portrait was drawn for every facial expression. Logistic and linear regression was performed to analyze the correlation between facial expression and clinical indicators, including QOL, disease activity and severity. The VGG-19 network model was utilized to discriminate facial expressions automatically.

Results

Two groups, i.e., the non-negative emotion (neutral, happy) and the negative emotion (disgust, angry, fear, sadness, surprise), and seven expressions of GO patients were systematically analyzed. Facial expression was statistically associated with GO activity (P = 0.002), severity (P < 0.001), QOL visual functioning subscale scores (P = 0.001), and QOL appearance subscale score (P = 0.012). The deep learning model achieved satisfactory results (accuracy 0.851, sensitivity 0.899, precision 0.899, specificity 0.720, F1 score 0.899, and AUC 0.847).

Conclusions

As a novel clinical sign, facial expression holds the potential to be incorporated into GO assessment system in the future. The discrimination model may assist clinicians in real-life patient care.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This study was supported by the National Key R&D Program of China (2018YFC1106100, 2018YFC1106101); Interdisciplinary Program of Shanghai Jiao Tong University (ZH2018QNA07, ZH2018ZDA12); the Science and Technology Commission of Shanghai (19410761100, 19DZ2331400); Cross-disciplinary Research Fund of Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine (JYJC202115); Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210901) and Shanghai Key Clinical Specialty, Shanghai Eye Disease Research Center (2022ZZ01003).

Author information

Author notes

  1. Chaoyu Lei and Mingyu Qu contributed equally to this work as co-first authors.

  2. Xuefei Song, Guangtao Zhai and Huifang Zhou contributed equally to this work as co-corresponding authors.

Authors and Affiliations

  1. Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    C. Lei, M. Qu, J. Huang, X. Song & H. Zhou

  2. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China

    C. Lei, M. Qu, J. Huang, X. Song & H. Zhou

  3. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

    H. Sun, J. Huang & G. Zhai

Authors
  1. C. Lei
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  2. M. Qu
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  3. H. Sun
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  4. J. Huang
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  5. J. Huang
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  6. X. Song
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  7. G. Zhai
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  8. H. Zhou
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Contributions

CL and MQ wrote the article. HS and JH performed the deep learning model training. CL and JH performed the statistical analysis and prepared the figures. XS, GZ, and HZ supervised the study and critically reviewed the article. CL and HZ designed the study. All co-authors have reviewed and approved the article before submission.

Corresponding authors

Correspondence to X. Song, G. Zhai or H. Zhou.

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Conflict of interest

The authors declare that there is no conflict of interest in this study.

Research involving human participants and/or animals

This study was approved by the local ethics committee (No. SH9H-2019-T8-1) in accordance with the Declaration of Helsinki 2013.

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A signed consent form was provided for the publication of any identifiable patient’ images.

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Lei, C., Qu, M., Sun, H. et al. Facial expression of patients with Graves’ orbitopathy. J Endocrinol Invest 46, 2055–2066 (2023). https://doi.org/10.1007/s40618-023-02054-y

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  • DOI: https://doi.org/10.1007/s40618-023-02054-y

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