Eyelid Diseases
Conditions
Brief 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.
Interventions
OTHERPhotography
Facial photographs and blinking videos are taken
Sponsors
Second Affiliated Hospital, School of Medicine, Zhejiang University
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
1. normal volunteers without eyelid diseases 2. patients with blepharoptosis 3. patients with blepharospasm 4. patients with dry eye disease 5. patients with Graves' disease
Exclusion criteria
variable ptosis (e.g., myasthenia gravis), entropion, ectropion, enophthalmos, exophthalmos, strabismus, and abnormalities of pupil
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| one-dimensional parameters of eyelid topology | through study completion, 5 years | Palpebral fissure length \[Margin reflex distance 1, Margin reflex distance 2\], Lid length \[Upper lid length, Lower lid length\], Multiple mid-pupil lid distances |
| two-dimensional parameters of eyelid topology | through study completion, 5 years | Palpebral fissure area \[Medial area, Corneal area, Lateral area\] |
| subtypes of levator function | through study completion, 5 years | Levator function is classified into three categories: good, fair and poor |
Countries
China
Contacts
Primary ContactJuan Ye
Backup ContactLixia Lou
Outcome results
None listed