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.
Study Type
OBSERVATIONAL
Enrollment
10,000
Various ophthalmic examination modalities, including slit lamp photography, fundus photography, optical coherence tomography imaging and optical coherence tomography angiography, etc.
Various pulmonary examination modalities, including radiography, chest CT, pulmonary function measurement, etc.
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
RECRUITINGGuangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd)
Guangzhou, Guangdong, China
RECRUITINGthe First Affiliated Hospital of Guangzhou Medical University
Guangzhou, Guangdong, China
RECRUITINGShenzhen Third People's Hospital
Shenzhen, Guangdong, China
RECRUITINGArea Under the Receiver Operating Characteristic curve
Determining the accuracy of diagnosing pulmonary disease with ophthalmic examination
Time frame: Through study completion, an average of 1 year
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