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Assisting Pulmonary Disease Diagnosis With Ophthalmic Artificial Intelligence Technology

NCT05847894 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 10000

Last updated 2025-05-23

No results posted yet for this study

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.

Conditions

  • Pulmonary Diseases
  • Ophthalmological Diagnostic Techniques
  • Artificial Intelligence

Interventions

DIAGNOSTIC_TEST

Ophthalmic examination

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

DIAGNOSTIC_TEST

Pulmonary Examination

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

Sponsors & Collaborators

  • The First Affiliated Hospital of Guangzhou Medical University

    collaborator OTHER

  • Shenzhen Third People's Hospital

    collaborator OTHER

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

    collaborator UNKNOWN

  • Zhongshan Ophthalmic Center, Sun Yat-sen University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-06-29
Primary Completion
2026-05-31
Completion
2026-05-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05847894 on ClinicalTrials.gov