Comparative Study of Human and Automated Screening for Antinuclear Antibodies by Immunofluorescence on HEp-2 Cells
Bibliographic record
Abstract
Background: Several automated systems had been developed in order to reduce inter-observer variability in indirect immunofluorescence (IIF) interpretation. We aimed to evaluate the performance of a processing system in antinuclear antibodies (ANA) screening on HEp-2 cells. Patients and Methods: This study included 64 ANA-positive sera and 107 ANA-negative sera that underwent IIF on two commercial kits of HEp-2 cells (BioSystems® and Euroimmun®). IIF results were compared with a novel automated interpretation system, the “CyclopusCADImmuno®” (CAD). Results: All ANA-positive sera images were recognized as positive by CAD (sensitivity = 100%), while 17 (15.9%) of the ANA-negative sera images were interpreted as positive (specificity = 84.1%), κ=0.799 (SD=0.045). Comparison of IIF pattern determination between human and CAD system revealed on HEp-2 (BioSystems®), a complete concordance in 6 (9.37%) sera, a partial concordance (sharing of at least 1 pattern) in 42 (65.6%) cases and in 16 (25%) sera the pattern interpretation was discordant. Similarly, on HEp-2 (Euroimmun®) the concordance in pattern interpretation was total in 5 (7.8%) sera, partial in 39 (60.9%) and absent in 20 (31.25%). For both tested HEp-2 cells kits agreement was enhanced for the most common patterns, homogenous, fine speckled and coarse speckled. While there was an issue in identification of nucleolar, dots and nuclear membranous patterns by CAD. Conclusion: Assessment of ANA by IIF on HEp-2 cells using the automated interpretation system, the “CyclopusCADImmuno®” is a reliable method for positive/negative differentiation. Continuous integration of IIF images would improve the pattern identification by the CAD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".