Automated classification of cerebral arteries and veins in the neonate using ultrafast doppler spectrogram
Bibliographic record
Abstract
Abstract Objective. Cerebral arterial and venous flow (A/V) classification is a key parameter for understanding dynamic changes in neonatal brain perfusion. Currently, transfontanellar ultrasound Doppler imaging is the reference clinical technique able to discriminate between A/V using vascular indices such as resistivity index (RI) or pulsatility index (PI). However, under conditions of slow arterial and venular flow, small signal fluctuations can lead to potential misclassifications of vessels. Recently, ultrafast ultrasound imaging has paved the way for better sensitivity and spatial resolution. Here, we show that A/V classification can be performed robustly using ultrafast Doppler spectrogram. Approach. The overall classification steps are as follows: for any pixel within a vessel, a normalized Doppler spectrogram (NDS) is computed that allows for normalized correlation analysis with ground-truth signals that were established semi-automatically based on anatomical/physiological references. Furthermore, A/V classification is performed by computing Pearson correlation coefficient between NDS in ground-truth domains and the individual pixel’s NDS inside vessels and finding an optimal threshold. Main Results. When applied to human newborns ( n = 40), the overall accuracy, sensitivity, and specificity were found to be 88.5% ± 6.7%, 88.5% ± 6.5%, and 87.0% ± 8.8% respectively. We also examined strategies to fully automate this process, leading to a moderate decrease of 1%–3% in the same metrics. Additionally, when compared to the main clinical metrics such as RI, and PI, the receiver operating characteristic curves exhibited higher areas under the curve; on average by +36% ( p < 0.0001) in the full imaging sector, +35% ( p = 0.0116) in the cortical regions, +53% ( p < 0.0001) in the basal ganglia, +28% ( p = 0.0051) in the cingulate gyrus, and +35% ( p < 0.0001) in the remaining brain structures. Significance: Our findings suggest that the proposed NDS-based approach can distinguish between A/V when studying cerebral perfusion in neonates.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".