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Realistic in Generation of HEp-2 Cell Images Using Latent Diffusion Models: a Multi-center Visual Turing Test

NCT06542783 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2024-08-07

No results posted yet for this study

Summary

The objective of this prospective observational study is to rigorously examine the feasibility and efficacy of utilizing latent diffusion models for data augmentation in anti-nuclear antibody (ANA) Hep-2 cell immunofluorescence images. The main question it aims to answer is:

Can the application of such models potentially enhance the data quality, increase sample diversity, or improve the accuracy and efficiency of subsequent analytical processes (like disease diagnosis and classification) when utilized with ANA-related images?

Conditions

  • Anti-nuclear Antibody
  • Visual Turing Tests
  • Artifical Intelligence

Interventions

BEHAVIORAL

referring to the results of AI model output

determining the ANA pattern type with or without referring to the results of AI model output.

Sponsors & Collaborators

  • Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

    lead OTHER

Principal Investigators

  • Guangyu Chen, PhD · Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-09-30
Primary Completion
2025-12-31
Completion
2026-06-30

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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 NCT06542783 on ClinicalTrials.gov