Autoimmune / Connective Tissue DiseasesNCT06542783
Realistic in Generation of HEp-2 Cell Images Using Latent Diffusion Models: a Multi-center Visual Turing Test
An observational study of Autoimmune / Connective Tissue Diseases and Anti-Nuclear Antibody, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Autoimmune / Connective Tissue Diseases, Anti-Nuclear Antibody, Visual Turing Tests
- What is being tested?
- referring to the results of AI model output
- Type of treatment
- Lifestyle or therapy programme
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 10 May 2025to 30 Jan 2029
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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 locations and contacts
1 site in 1 country.
China1 site
Department of Clinical Laboratory, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Shanghai, Shanghai Municipality, 200092
About this study
Official title: Evaluating the Realism of ANA HEp-2 Cell Images Synthesized 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?
Read the 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.
- Conditions
- Autoimmune / Connective Tissue Diseases, Anti-Nuclear Antibody, Visual Turing Tests, Artifical Intelligence
- Being tested
- referring to the results of AI model output
- Keywords
- anti-nuclear antibody, latent diffusion models, Visual Turing tests
- Medical subject headings
- Connective Tissue Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
referring to the results of AI model outputBehavioural
determining the ANA pattern type with or without referring to the results of AI model output.
| Study group | Role | What they receive |
|---|---|---|
| experts | with over 20 years of experience in ANA-IIF reading Receives: Behavioral: referring to the results of AI model output | |
| junior cytopathologists | less than 5 years of academic medical experience Receives: Behavioral: referring to the results of AI model output |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Baseline
Secondary outcomes (2)
The impact of the AI model's output on the participants
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.
Measured: Baseline
The time taken of ANA pattern interpretation
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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Aug 2024
- Study start10 May 2025
- Primary completion30 Dec 2028 (estimated)
- Study completion30 Jan 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Autoimmune / Connective Tissue DiseasesStudies of the same condition, open and under way.
- More trials run by Xinhua Hospital, Shanghai Jiao Tong University School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Guangyu Chen, PhD, Study director, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06542783
- Sponsor's protocol ID
- XH-24-007
- Status last verified
- Jul 2026
- First submitted
- 23 Jul 2024
- Last updated
- 10 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 38279651Rahman 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.
PubMed 32745976Hobson 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.
PubMed 26303104Niehues 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.
PubMed 38678938Selim 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.
PubMed 38222387Marouf 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.
PubMed 31919373
Study at a glance
From the registry record, last updated by the sponsor on 10 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models: A Multi-center Visual Turing Test
- ClinicalTrials.gov ID
- NCT06542783
- Acronym / sponsor ID
- XH-24-007
- Conditions
- Autoimmune / Connective Tissue Diseases, Anti-Nuclear Antibody, Visual Turing Tests, Artifical Intelligence
- Keywords
- anti-nuclear antibody, latent diffusion models, Visual Turing tests
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 10 May 2025 (actual)
- Primary completion
- 30 Dec 2028 (estimated)
- Study completion
- 30 Jan 2029 (estimated)
- First posted
- 7 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| experts | – | Behavioral: referring to the results of AI model outputwith over 20 years of experience in ANA-IIF reading |
| junior cytopathologists | – | Behavioral: referring to the results of AI model outputless than 5 years of academic medical experience |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| referring to the results of AI model outputdetermining the ANA pattern type with or without referring to the results of AI model output. | Behavioural | experts, junior cytopathologists |
Primary outcome
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).
2 secondary outcomes
The impact of the AI model's output on the participants
Time frame: Baseline
The time taken of ANA pattern interpretation
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Sep 2026.
- Background: PubMed 38279651Zeng 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.
- Background: PubMed 32745976Rahman 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.
- Background: PubMed 26303104Hobson 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.
- Background: PubMed 38678938Niehues 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.
- Background: PubMed 38222387Selim 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.
- Background: PubMed 31919373Marouf 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.
- Our platform for the Visual Turing TestLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Guangyu Chen, PhD study director
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Autoimmune / Connective Tissue Diseases and Anti-Nuclear Antibody, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 10 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The realistic of images synthesized by diffusion models (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06542783, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Sep 2026.