Clinical trial NCT06542783: Realistic in Generation of HEp-2 Cell Images Using Latent Diffusion Models: a Multi-center Visual Turing Test
Official title: Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models: A Multi-center Visual Turing Test
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Anti-nuclear Antibody
Visual Turing Tests
Artifical Intelligence
Keywords: anti-nuclear antibody
latent diffusion models
Visual Turing tests
Lead sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2024-08-07
Enrollment: 300
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: We are recruiting cytopathologists from clinical laboratories in multiple medical institutions worldwide who specialize in interpreting anti-nuclear antibody (ANA) patterns to participate in a visual Turing test.

Brief 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?

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: The realistic of images synthesized by diffusion models
timeFrame: Baseline
Secondary outcomes:
measure: The impact of the AI model's output on the participants
timeFrame: Baseline
measure: The time taken of ANA pattern interpretation
timeFrame: Baseline
Other outcomes:
Locations:
Study officials:
affiliation: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
name: Guangyu Chen, PhD
role: STUDY_DIRECTOR
References:
citation: Zeng J, Gao X, Gao L, Yu Y, Shen L, Pan X. Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework. Brief Bioinform. 2024 Jan 22;25(2):bbad531. doi: 10.1093/bib/bbad531.
citation: Rahman S, Wang L, Sun C, Zhou L. Deep learning based HEp-2 image classification: A comprehensive review. Med Image Anal. 2020 Oct;65:101764. doi: 10.1016/j.media.2020.101764. Epub 2020 Jul 7.
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citation: Marouf M, Machart P, Bansal V, Kilian C, Magruder DS, Krebs CF, Bonn S. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat Commun. 2020 Jan 9;11(1):166. doi: 10.1038/s41467-019-14018-z.