Actively Recruiting
Assessment of Eyelid Topology and Kinetics Based on Deep Learning Method
Led by Second Affiliated Hospital, School of Medicine, Zhejiang University · Updated on 2021-06-10
500
Participants Needed
1
Research Sites
313 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
Assessment of Eyelid Topology and Kinetics Based on Deep Learning Method
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Normal volunteers without eyelid diseases
- Patients with blepharoptosis
- Patients with blepharospasm
- Patients with dry eye disease
- Patients with Graves' disease
You will not qualify if you...
- Variable ptosis such as myasthenia gravis
- Entropion
- Ectropion
- Enophthalmos
- Exophthalmos
- Strabismus
- Abnormalities of pupil
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Juan Ye
Hangzhou, Zhejiang, China, 310000
Actively Recruiting
Research Team
J
Juan Ye
CONTACT
L
Lixia Lou
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
5
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