Kinematics of the Upper Eyelid and the Globe During Downward Excursion With Comparative Analysis in Patients With Thyroid Eye Disease

The von Gräfe sign measurement

PURPOSE

To measure the amplitude of upper eyelid saccadic movements in patients with Graves' retraction.

METHODS

The amplitude and velocity of lid saccades were quantified using customized software in 36 patients with Graves' upper eyelid retraction (GO) and in 32 subjects of a control group (CG). All patients were in the chronic phase of the disease and had a controlled thyroid status. Eyelid retraction was measured using the software ImageJ. Levator palpebrae superioris muscle (LPSM) dimensions were measured through orbital computed tomography (CT).

RESULTS

The mean MRD1 was 6.5 mm and 3.9 mm in the GO and CG groups, respectively. The LPSM was radiologically enlarged in patients. The absolute values of both amplitude and velocity of lid movement did not differ between the two groups. However, considering the final position of the lid margin in relation to the pupil in downgaze, the margin-reflex distance of the patients was significantly higher than that of the controls.

CONCLUSION

In patients with upper eyelid retraction in inactive GO the relaxation of the LPSM are not affected, despite the muscle enlargement. However, the amplitude of the downward lid movement of the patients does not compensate for the lid retraction. The final position of the lid margin of the patients in downgaze is significantly higher than in controls, indicating the inability of the LPMS relaxation to overcome lid retraction. The von Gräfe sign is thus a relative phenomenon and not an absolute indication of loss of the lid elastic properties.

An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images

Background

Thyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients' appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO.

Method

The study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.

Results

The proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.

Conclusion

The ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.