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Related Concept Videos

Graves' Disease I: Introduction01:28

Graves' Disease I: Introduction

Graves' disease is an autoimmune disorder that causes hyperthyroidism, or overactivity of the thyroid gland. It results from autoantibodies called thyroid-stimulating immunoglobulins (TSIs), which bind to thyroid-stimulating hormone (TSH) receptors, leading to overstimulation of hormone production and a hypermetabolic state.EtiologyAlthough considered idiopathic, Graves’ disease has well-established contributing factors. There is a strong genetic component, with increased prevalence in...
Graves Disease II: Pathophysiology01:24

Graves Disease II: Pathophysiology

Graves’ disease is an autoimmune disorder characterized by the production of thyroid-stimulating immunoglobulins (TSI) that activate TSH receptors, leading to excessive synthesis and release of thyroid hormones (T3 and T4) and resulting in hyperthyroidism.Among all causes of hyperthyroidism, Graves’ disease is the most common and can happen at any age, though it is more frequent in women. It produces a hypermetabolic state with features such as weight loss, tachycardia, tremor, and heat...

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Facial Aging in Thyroid Eye Disease: Quantification by Artificial Intelligence.

Persiana S Saffari1, Jason C Strawbridge2, Kelsey A Roelofs3

  • 1Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami, Miami, FL.

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Summary

Thyroid eye disease (TED) alters perceived facial aging, making patients appear older initially and younger later. Artificial intelligence (AI) analysis revealed significant differences compared to controls, highlighting TED's impact on facial appearance.

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Facial Aging Research

Background:

  • Thyroid eye disease (TED) is an autoimmune condition affecting the tissues around the eye.
  • Facial aging is a complex process influenced by genetics, environment, and health conditions.
  • Artificial intelligence (AI) offers novel tools for objective assessment of facial changes.

Purpose of the Study:

  • To investigate the impact of thyroid eye disease (TED) on perceived facial aging using artificial intelligence (AI).
  • To compare AI-based age estimation with expert assessment in TED patients and controls.
  • To analyze longitudinal changes in facial aging associated with TED.

Main Methods:

  • A cross-sectional cohort study analyzing standardized facial photographs of TED patients and age-matched controls.
  • Utilized a pre-trained AI model to infer chronological age from facial images at initial and final visits (over 5 years follow-up).
  • Compared AI-inferred ages with chronological ages and expert (oculoplastic surgeons) estimations.

Main Results:

  • AI initially inferred TED patients to be 4.3 years older than their actual age (vs. 0.63 years for controls, P=0.005).
  • At final follow-up, AI inferred TED patients to be 5.0 years younger (vs. 1.4 years younger for controls, P=0.004).
  • The mean age difference change was significantly greater in TED patients (9.3 years) than controls (2.0 years, P<0.001).

Conclusions:

  • AI analysis indicates TED significantly alters perceived facial aging, initially making patients appear older and later younger.
  • These changes may relate to initial pathologic soft tissue expansion and subsequent age-related deflation in TED.
  • AI demonstrates potential for objective, accurate age estimation, outperforming human experts in certain scenarios.