Review of Biomedical Applications of Contactless Imaging of Neonates Using Infrared Thermography and Beyond
Bibliographic record
Abstract
The sick preterm infant monitoring is an intriguing job that medical staff in Neonatal Intensive Care Units (NICU) must deal with on a daily basis. As a standards monitoring procedure, preterm infants are monitored via sensors and electrodes that are firmly attached to their fragile and delicate skin and connected to processing monitors. However, an alternative exists in contactless imaging to record such physiological signals (we call it as Physio-Markers), detecting superficial changes and internal structures activities which can be used independently of, or aligned with, conventional monitors. Countless advantages can be gained from unobtrusive monitoring not limited to: (1) quick data generation; (2) decreasing physical and direct contact with skin, which reduces skin breakdown and minimizes risk of infection; and (3) reduction of electrodes and probes connected to clinical monitors and attached to the skin, which allows greater body surface-area for better care. This review is an attempt to build a solid ground for and to provide a clear perspective of the potential clinical applications of technologies inside NICUs that use contactless imaging modalities such as Visible Light Imaging (VLI), Near Infrared Spectroscopy (NIRS), and Infrared Thermography (IRT).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".