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?
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
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
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.
Cohort
Prospective
referring to the results of AI model output
Behavioraldetermining the ANA pattern type with or without referring to the results of AI model output.
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).
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.
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.
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This trial is not recruiting at the moment. You can still explore other options:
