Assessment of Eyelid Topology and Kinetics Based on Deep Learning Method

Zhejiang University

Status: Enrolling

Conditions: Eyelid Diseases

Treatments: Other: Photography

Study type: Observational

Funder types: Other

Identifiers

NCT04921020

2020-485

Details and patient eligibility

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.

Enrollment: 500 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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

Trial design

500 participants in 5 patient groups

Normal participants

Treatment:

Other: Photography

Patients with blepharoptosis

Treatment:

Other: Photography

Patients with blepharospasm

Treatment:

Other: Photography

Patients with dry eye disease

Treatment:

Other: Photography

Patients with Graves' disease

Treatment:

Other: Photography