Assessment of Eyelid Topology and Kinetics Based on Deep Learning Method
Department of Ophthalmology
Second Affiliated Hospital, School of Medicine, Zhejiang University
500 participants
Aug 1, 2020
OBSERVATIONAL
Conditions
Summary
This study plans to assess eyelid topology (such as margin reflex distance, eyelid contour, and corneal exposure area) and blinking (such as frequency, velocity, and duration), using deep learning method to automatically extract eyelid topological features, and to predict subtypes of levator function, using deep learning method to extract blinking features, in order to provide new ideas and means to assess eyelid topology and kinetics.
Eligibility
Inclusion Criteria5
- normal volunteers without eyelid diseases
- patients with blepharoptosis
- patients with blepharospasm
- patients with dry eye disease
- patients with Graves' disease
Exclusion Criteria1
- variable ptosis (e.g., myasthenia gravis), entropion, ectropion, enophthalmos, exophthalmos, strabismus, and abnormalities of pupil
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Interventions
Facial photographs and blinking videos are taken
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT04921020