ArticleJournal of endocrinological investigation2023
Facial expression of patients with Graves' orbitopathy.
Article in Journal of endocrinological investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Artificial intelligence and patient reported outcomes in ophthalmology.Journal of patient-reported outcomes · 2026Pooled it
- ProptoView: AI-based digital exophthalmometry using multi-view facial images in a multinational validation study.Journal of translational medicine · 2026Article
- Evaluating and enhancing the performance of large language models in thyroid eye disease through customization and Chain-of-Thought strategies.Scientific reports · 2026Article
- Management of Thyroid Eye Disease: A Comparison Between Three Recent Clinical Guidelines.Ophthalmology and therapy · 2026Review
- Sequential sensitivity analysis of multimodal large language models for rare orbital disease detection.Communications medicine · 2026Article
- Real-world performance of the AI diagnostic system IDx-DR in the diagnosis of diabetic retinopathy and its main confounders.Scientific reports · 2026Article
- Enhancing ocular sign detection: AI-based strategic segmentation for improved accuracy and privacy protection.NPJ digital medicine · 2026Article
- Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Development of a neural network-based automated eyelid measurement system.Scientific reports · 2024Article
- Advances in artificial intelligence in thyroid-associated ophthalmopathy.Frontiers in endocrinology · 2024Review
- The role of Whitnall's ligament position in the success of levator resection surgery in congenital ptosis.BMC ophthalmology · 2023Article
- Introduction of Deep Learning-Based Infrared Image Analysis to Marginal Reflex Distance1 Measurement Method to Simultaneously Capture Images and Compute Results: Clinical Validation Study.Journal of clinical medicine · 2023Article
- Sector area index: a novel supporting marker for blepharoptosis screening and grading.International ophthalmology · 2023Article
- Neural network application for assessing thyroid-associated orbitopathy activity using orbital computed tomography.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
purposePatients 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.
methodsFacial 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.
resultsTwo 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).
conclusionsAs 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.