Classification of blood cells and tumor cells using label‐free ultrasound and photoacoustics
Bibliographic record
Abstract
A label-free method that can identify cells in a blood sample using high frequency photoacoustic and ultrasound signals is demonstrated. When the wavelength of the ultrasound or photoacoustic wave is similar to the size of a single cell (frequencies of 100-500 MHz), unique periodic features occur within the ultrasound and photoacoustic power spectrum that depend on the cell size, structure, and morphology. These spectral features can be used to identify different cell types present in blood, such as red blood cells (RBCs), white blood cells (WBCs), and circulating tumor cells. Circulating melanoma cells are ideal for photoacoustic detection due to their endogenous optical absorption properties. Using a 532 nm pulsed laser and a 375 MHz transducer, the ultrasound and photoacoustic signals from RBCs, WBCs, and melanoma cells were individually measured in an acoustic microscope to examine how the signals change between cell types. A photoacoustic and ultrasound signal was detected from RBCs and melanoma cells; only an ultrasound signal was detected from WBCs. The different cell types were distinctly separated using the ultrasound and photoacoustic signal amplitude and power spectral periodicity. The size of each cell was also estimated from the spectral periodicity. For the first time, sound waves generated using pulse-echo ultrasound and photoacoustics have been used to identify and size single cells, with applications toward counting and identifying cells, including circulating melanoma cells.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".