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

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?

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Key information

Conditions

Anti-nuclear Antibody Artifical Intelligence Visual Turing Tests

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

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

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

Treatment and study plan

referring to the results of AI model output

Behavioral

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

Primary outcomes

The realistic of images synthesized by diffusion models

Time frame: Baseline

The investigators conducted a study using the visual Turing test method, measuring through a questionnaire format, and assessed the measurement results using a 5-point Likert Scale.

The 5-point Likert Scale assesses participants' opinions on the quality of images through five response options: Real, Much like, Uncertain, Not quite like, Fake. It calculates scores by assigning numbers (e.g., 5 to 1) to these options, summing up scores for each participant. Results are evaluated by analyzing the distribution of scores, including mean scores, and assessing their reliability and validity.

Additionally, the investigator calculated a range of parameters utilized for internal model assessment, including: including precision, recall, F1 score, and mean average precision (mAP).

Secondary outcomes

The impact of the AI model's output on the participants

Time frame: Baseline

The investigator evaluated the change in the accuracy rate of participants' interpretations before and after being assisted by AI model, investigators will conduct a comparative analysis. Additionally, the investigator calculate the Kappa coefficient of agreement between human interpretations and the model, and evaluate whether there are differences in accuracy among cytopathologists with varying levels of experience when assisted by AI.
The time taken of ANA pattern interpretation

Time frame: Baseline

The investigator compare the time taken of participant to complete interpretations before and after the AI model's intervention, assessing whether there is a reduction in average interpretation time per case, from X minutes pre-AI assistance to Y minutes post-AI.

Study contacts

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

Junxiang Zeng, Dr

CONTACT

[email protected]

+8613162232879

Sponsors and collaborators

Lead sponsor

Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

Other

Registry information

Official study title

Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models: A Multi-center Visual Turing Test

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2026
First posted: Aug 7, 2024
Registry last updated: Aug 7, 2024

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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