Machine Vision for Patient Monitoring in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Continuous patient monitoring of newborns in the neonatal intensive care unit (NICU) is often performed with wired sensors which can be cumbersome, can interfere with parental bonding, and can irritate the patient's fragile skin. Non-contact video-based patient monitoring systems are therefore a preferrable solution. While a multitude of high-performing machine vision technologies have been successfully implemented on an adult population, such methods often fail in neonatal population. In this thesis, we assess state-of-the-art adult-based methods to bridge the gap to an understudied neonatal population in the NICU environment. To this end, several important machine vision concepts are investigated, including scene understanding, image classification, face detection, semantic segmentation, motion detection, face tracking, and heart rate estimation. In each of these areas, we assess the state-of-the-art and identify its applicability to a neonatal population. In cases where serious limitations are observed, this thesis pushes the state-of-the-art and implements new techniques more suitable for newborns. Finally, a non-contact neonatal heart rate monitoring pipeline is created using multiple research contributions in this thesis. Doing so, we obtain a vital sign decision support tool for clinical use by estimating the uncertainty in each research contributions and demonstrating how errors can propagate from one to another.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".