RecruitingNCT04921020

Assessment of Eyelid Topology and Kinetics Based on Deep Learning Method

Department of Ophthalmology


Sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Enrollment

500 participants

Start Date

Aug 1, 2020

Study Type

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

OTHERPhotography

Facial photographs and blinking videos are taken


Locations(1)

Juan Ye

Hangzhou, Zhejiang, China

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NCT04921020


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