Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology

Artificial Intelligence Clinical Trial

Official title:

Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05847894
• Other study ID #: 2023KYPJ111
• Status: Not yet recruiting
• Start date: July 2023
• Est. completion date: May 2024

Study information

• Verified date: April 2023
• Source: Zhongshan Ophthalmic Center, Sun Yat-sen University
• Contact: Weixing Zhang, M.D.
• Phone: 8615602211660
• Email: zhangwx98[@]mail2.sysu.edu.cn
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 10000
• Est. completion date: May 2024
• Est. primary completion date: May 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Lung Diseases
• Pulmonary Diseases

Intervention

Diagnostic Test:
• 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

Country	Name	City	State
China	Zhongshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (2)

Lead Sponsor	Collaborator
Zhongshan Ophthalmic Center, Sun Yat-sen University	The First Affiliated Hospital of Guangzhou Medical University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Area Under the Receiver Operating Characteristic curve	Determining the accuracy of diagnosing pulmonary disease with ophthalmic examination	Through study completion, an average of 1 year