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Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology
Sponsor: Zhongshan Ophthalmic Center, Sun Yat-sen University
Summary
This study intends to collect ophthalmologic examination results, pulmonary examination results and related indexes from patients with pulmonary disease and control populations, and combine big data analysis and artificial intelligence technology to explore whether new methods can be provided for early screening strategies for pulmonary disease with the aid of ophthalmologic examination, and thus assist in identifying the types of pulmonary disease and determining disease prognosis.
Key Details
Gender
All
Age Range
Any - Any
Study Type
OBSERVATIONAL
Enrollment
10000
Start Date
2020-06-29
Completion Date
2026-05
Last Updated
2025-05-23
Healthy Volunteers
Yes
Interventions
Ophthalmic examination
Various ophthalmic examination modalities, including slit lamp photography, fundus photography, optical coherence tomography imaging and optical coherence tomography angiography, etc.
Pulmonary Examination
Various pulmonary examination modalities, including radiography, chest CT, pulmonary function measurement, etc.
Locations (4)
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
Guangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd)
Guangzhou, Guangdong, China
the First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, China
Shenzhen Third People's Hospital
Shenzhen, Guangdong, China