Leukocyte Activation Assay Using AI-Enhanced Digital Holographic Microscopy
Bibliographic record
Abstract
Digital holographic microscopy (DHM) is a label-free and high-throughput cellular imaging technology leverages phase contrast to reveal subtle intercellular refractive index variations, allowing to the derivation of biophysical parameters related to cell function.Neutrophil and monocyte activation, major parts of the innate immune system, can be quantified via fluorescence flow cytometry by measuring activation markers, aiding in the diagnosis and assessment of inflammatory diseases.However, workflow standardization and quantification with fluorescence flow cytometry remain laborious, challenging, and costly.Automated haematology analysers are widely available, solve labour challenges, and provide economical differential leukocyte counts but cannot differentiate or assess leukocyte activation.There is an unmet need to provide reliable information on immune cell function at the bedside using pointof-care diagnostics.DHM potentially meets this need by providing label-free, automated, high-throughput and high-resolution cellular imaging capable of monitoring leukocyte activation.Furthermore, integrating deep learning models can be used to accurately recognize activated cells, reducing operator variability and automating analysis.We present a novel imaging platform utilizing DHM to identify activated leukocytes as an early predictive biomarker for inflammatory states, demonstrating this technology may enable real-time patient monitoring in clinical settings, facilitating risk stratification and therapy response monitoring at the bedside.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".