Artificial Intelligence, Ophthalmological Diagnostic Techniques, Pulmonary Diseases
Conditions
Keywords
Pulmonary Diseases, Ophthalmological Diagnostic Techniques, Artificial intelligence
Brief 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.
Interventions
DIAGNOSTIC_TESTOphthalmic examination
Various ophthalmic examination modalities, including slit lamp photography, fundus photography, optical coherence tomography imaging and optical coherence tomography angiography, etc.
DIAGNOSTIC_TESTPulmonary Examination
Various pulmonary examination modalities, including radiography, chest CT, pulmonary function measurement, etc.
Sponsors
Zhongshan Ophthalmic Center, Sun Yat-sen University
The First Affiliated Hospital of Guangzhou Medical University
Shenzhen Third People's Hospital
Guangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd), Guangzhou, China
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Those aged ≥18 years; or those aged \<18 years who can cooperate with the relevant examination and are accompanied and informed by a guardian; * People with respiratory-related diseases who were to undergo pulmonary examination, or those who volunteered to participate in the trial through publicity recruitment; * expected survival time of 3 months or more; * Those with no previous serious underlying disease and no history of serious eye disease; * Those who can cooperate with ophthalmologic and pulmonary-related examinations and have regular follow-up examinations; * Those who gave informed consent to the study prior to the trial and voluntarily signed the informed consent form; * Other conditions that can be included in the study as judged by the investigator.
Exclusion criteria
* Patients who are unable to complete ophthalmology or pulmonary-related examinations and regular follow-ups due to serious diseases, trauma or surgery (serious ophthalmology diseases such as extremely poor vision that cannot be fixed, ocular atrophy, severe refractive interstitial clouding that prevents fundus photography, etc.); * People with poor compliance due to various reasons such as alcohol or drug dependence, or mental disorders; * Those without informed consent; * Other conditions judged by the investigator to be unsuitable for participation in the trial.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Receiver Operating Characteristic curve | Through study completion, an average of 1 year | Determining the accuracy of diagnosing pulmonary disease with ophthalmic examination |
Countries
China
Contacts
CONTACTWeixing Zhang, M.D.
Outcome results
None listed