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Related Experiment Video

Updated: May 28, 2026

Intense Pulsed Light for the Treatment of Dry Eye Owing to Meibomian Gland Dysfunction
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Marker-Free, Automated Eyelid Assessment in Thyroid Eye Disease Using Artificial Intelligence: A Multicenter

Jae Hoon Moon1,2,3, Jongchan Kim1, Joonhyeon Park1

  • 1THYROSCOPE INC., Ulsan, Republic of Korea.

Ophthalmology Science
|May 27, 2026
PubMed
Summary

A new AI software, Glandy LID, accurately measures eyelid morphology using corneal diameter, eliminating the need for physical markers. This tool offers a reliable method for monitoring thyroid eye disease (TED) in clinical and remote settings.

Keywords:
Artificial intelligenceDeep learningEyelid retractionTelemedicineThyroid eye disease

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Thyroid eye disease (TED) management requires accurate eyelid morphology assessment.
  • Current methods for measuring eyelid parameters like margin reflex distance (MRD1, MRD2) can be subjective or require physical markers.
  • Automated, objective, and marker-free solutions are needed for consistent TED monitoring.

Purpose of the Study:

  • To validate Glandy LID, a novel deep learning-based software for automated eyelid morphology assessment.
  • To evaluate the software's accuracy and generalizability using intrinsic corneal diameter for calibration.
  • To establish Glandy LID as a reliable, marker-free tool for clinical use.

Main Methods:

  • A multicenter retrospective study involving two cohorts (n=119 and n=140) with TED.
  • An AI algorithm segmented eyelid and corneal regions from facial photographs (DSLR and smartphone images).
  • Eyelid metrics were calculated using corneal diameter for scale; internal validation used physical markers, external validation used clinical records.

Main Results:

  • Internal validation showed high geometric precision (IoU 0.94) and excellent agreement for MRD1 (PCC 0.98, MAPE 5.69%) and MRD2 (PCC 0.94, MAPE 4.57%).
  • External validation with smartphone images demonstrated strong reliability for MRD1 (PCC 0.94, MAPE 9.06%) and MRD2 (PCC 0.93, MAPE 15.07%).
  • The AI system maintained high accuracy despite variations in image source and measurement comparison methods.

Conclusions:

  • Glandy LID provides an accurate, robust, and automated solution for eyelid measurement without physical markers.
  • The software overcomes limitations of manual and marker-based assessments, offering a practical tool for TED monitoring.
  • This AI-driven approach enhances accessibility for objective TED assessment in routine and remote healthcare.