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

NCT06542783 | Not Yet Recruiting

• 1 other identifier: XH-24-007
• Study Type: observational
• Target: 300
• Locations: 0 countries
• Sites: N/A

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?

Conditions

Anti-nuclear Antibody; Visual Turing Tests; Artifical Intelligence

Keywords

anti-nuclear antibody; latent diffusion models; Visual Turing tests

Outcome Measures

Primary Outcomes (1)

• The realistic of images synthesized by diffusion models

  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).

  Baseline

Secondary Outcomes (2)

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

  Baseline

• The time taken of ANA pattern interpretation

  Baseline

Study Arms (2)

experts

with over 20 years of experience in ANA-IIF reading

Behavioral: referring to the results of AI model output

junior cytopathologists

less than 5 years of academic medical experience

Behavioral: referring to the results of AI model output

Interventions

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.

experts; junior cytopathologists

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Xinhua Hospital, Shanghai Jiao Tong University School of Medicine: lead

Related Publications (6)

• 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.

  PMID: 38279651 BACKGROUND

• 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.

  PMID: 32745976 BACKGROUND

• Hobson P, Lovell BC, Percannella G, Vento M, Wiliem A. Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset. Artif Intell Med. 2015 Nov;65(3):239-50. doi: 10.1016/j.artmed.2015.08.001. Epub 2015 Aug 13.

  PMID: 26303104 BACKGROUND

• Niehues JM, Muller-Franzes G, Schirris Y, Wagner SJ, Jendrusch M, Kloor M, Pearson AT, Muti HS, Hewitt KJ, Veldhuizen GP, Zigutyte L, Truhn D, Kather JN. Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance. Comput Biol Med. 2024 Jun;175:108410. doi: 10.1016/j.compbiomed.2024.108410. Epub 2024 Apr 4.

  PMID: 38678938 BACKGROUND

• Selim M, Zhang J, Brooks MA, Wang G, Chen J. DiffusionCT: Latent Diffusion Model for CT Image Standardization. AMIA Annu Symp Proc. 2024 Jan 11;2023:624-633. eCollection 2023.

  PMID: 38222387 BACKGROUND

• 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.

  PMID: 31919373 BACKGROUND

Related Links

• Our platform for the Visual Turing Test

Study Officials

• Guangyu Chen, PhD

  Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

  STUDY DIRECTOR

Central Study Contacts

Junxiang Zeng, Dr

CONTACT

+8613162232879; zengjunxiang@xinhuamed.com.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 23, 2024
• First Posted: August 7, 2024
• Study Start: September 1, 2024
• Primary Completion: December 1, 2025
• Study Completion (Estimated): June 1, 2026
• Last Updated: August 7, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share