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

Shanghai Jiao Tong University

Status: Not yet enrolling

Conditions: Artifical Intelligence; Anti-nuclear Antibody; Visual Turing Tests

Treatments: Behavioral: referring to the results of AI model output

Study type: Observational

Funder types: Other

Identifiers

NCT06542783

XH-24-007

Details and patient eligibility

About

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?

Full description

A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, The investigators propose to use unsupervised learning with latent diffusion models for the realistic generation of ANA-IIF image data.

The investigators hypothesize that the the generation of ANA-IIF image will be realistic if it is hard to differentiate them (fake) from real (true) . To test this hypothesis, the investigators present a Multi-center Visual Turing tests (https://turing.rednoble.net/) in order to evaluate the quality of the generated (fake) images.

This experimental setup allows the investigators to validate the overall quality of the generated ANA-IIF images, which can then be used to (1) train cytopathologists for educational purposes, and (2) generate realistic samples to train deep networks with big data.

Enrollment: 300 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Originating from reputable medical institutions
• Possessing relevant certification and qualifications
• Having over one year of experience in interpreting anti-nuclear antibody (ANA) patterns within a laboratory setting

Exclusion criteria

• Lacking relevant professional certification and qualifications
• Without experience in interpreting ANA patterns
• Unwilling to accept the rules and informed consent of the visual Turing test

Trial design

300 participants in 2 patient groups

experts

Description:

with over 20 years of experience in ANA-IIF reading

Treatment:

Behavioral: referring to the results of AI model output

junior cytopathologists

Description:

less than 5 years of academic medical experience

Treatment:

Behavioral: referring to the results of AI model output