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NCT Number: NCT05847894

Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology

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.

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Key information

Sex eligibility

All sexes

Study type

Observational

Primary location

Guangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd), Guangzhou, Guangdong, China

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Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Ophthalmic examination

Diagnostic Test

Various ophthalmic examination modalities, including slit lamp photography, fundus photography, optical coherence tomography imaging and optical coherence tomography angiography, etc.

Pulmonary Examination

Diagnostic Test

Various pulmonary examination modalities, including radiography, chest CT, pulmonary function measurement, etc.

Primary outcomes

  1. Area Under the Receiver Operating Characteristic curve

    Time frame: Through study completion, an average of 1 year

    Determining the accuracy of diagnosing pulmonary disease with ophthalmic examination

Study contacts

Contact information is provided by the study sponsor or research team.

Weixing Zhang, M.D.

CONTACT

[email protected]

8615602211660

Sponsors and collaborators

Lead sponsor

Zhongshan Ophthalmic Center, Sun Yat-sen University

Other

Collaborators

  • Guangzhou Kindness Health Care Center (Guangzhou Jiubang Shanxin Clinic Ltd), Guangzhou, China
  • Shenzhen Third People's Hospital
  • The First Affiliated Hospital of Guangzhou Medical University

Registry information

Important dates

Study start
2020
Primary completion
2026
Study completion
2026
First posted
May 8, 2023
Registry last updated
May 23, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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