Validation of automatic scanning of microscope slides in recovering rare cellular events: application for detection of fetal cells in maternal blood
Bibliographic record
Abstract
OBJECTIVE: Detection of rare fetal cells (FCs) in the maternal circulation could be used for non-invasive prenatal diagnosis. Considering that FCs in maternal blood are present in extremely low frequency, manual scanning is cumbersome, time-consuming, and unsuitable for clinical applications. As an alternative, we optimized a custom-made classifier for automatic detection of FCs. METHODS: Using MetaSystems' automated platform, we developed a robust detection algorithm and validated its efficiency on retrieval of rare XY cells in a pure population of XX cells. Slides were scanned for presence of predefined XY cells after fluorescence in situ hybridization (FISH) and primed in situ labeling (PRINS). Retrieval of FCs was also performed on samples from maternal blood. RESULTS: The efficiency of detection of rare XY cells was 88% using FISH (117/133) in comparison with 78% (53/68) with PRINS. FC frequencies per 1 mL of maternal blood ranged from 3 to 6 FCs in normal pregnancies versus 13 to 21 FCs in Down syndrome pregnancies. CONCLUSION: Automatic scanning was more efficient and consistent than manual scanning for detection of rare FCs and required considerably less operator time. Automatic scanning using FISH is more sensitive than that using PRINS. The study validates automatic scanning retrieval of FCs from maternal blood.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".