Generalisation of a deep learning-based exophthalmometry system using facial photographs for non-thyroidal orbital disease.

Authors

Joonhyeon Park, Hyeong Ju Byeon, Kyubo Shin, HyunJo Jang, Jongchan Kim, Jaemin Park, Jin Sook Yoon, Jae Hoon Moon, JaeSang Ko

Published in

The British journal of ophthalmology. Sep 15, 2026. Epub Sep 15, 2026.

Abstract

To evaluate agreement between photo-based automated exophthalmometry and manual Hertel exophthalmometry in healthy individuals and patients with non-thyroidal orbital disease, with a deep learning system trained exclusively in thyroid eye disease (TED) cohorts applied without retraining or fine-tuning.
This was a retrospective observational study. A total of 269 individuals were included in the study, including 95 healthy controls and 174 patients with unilateral non-TED orbital pathology. Automated measurements obtained using Glandy EXO from same-day frontal facial photographs were compared with corresponding manual Hertel exophthalmometry measurements. Agreement was assessed using Pearson correlation coefficient (PCC), mean absolute error (MAE), intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic performance for clinically significant intereye asymmetry (>2.0 mm) was also evaluated.
The system showed good agreement with Hertel exophthalmometry in the eyes of healthy controls (PCC 0.80; MAE 0.86 mm; ICC 0.80) as well as the affected (PCC 0.81; MAE 1.41 mm; ICC 0.75) and fellow (PCC 0.76; MAE 1.03 mm; ICC 0.75) eyes of patients. Overall, 84.8% of measurements were within 2.0 mm of the manual Hertel values. The sensitivity, specificity and accuracy of the system for detecting clinically significant intereye asymmetry were 59.6%, 95.0% and 83.3%, respectively.
The TED-trained photoexophthalmometry system showed good agreement with Hertel measurements in healthy individuals and patients with non-TED orbital disease. These findings support its use as an adjunct to clinical examination for objective quantification and documentation of globe position.

PMID:
42744591
Bibliographic data and abstract were imported from PubMed on 16 Sep 2026.

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