Depth-Based Intervention Detection in the Neonatal Intensive Care Unit Using Vision Transformers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Depth cameras can provide an effective, noncontact, and privacy-preserving means to monitor patients in the Neonatal Intensive Care Unit (NICU). Clinical interventions and routine care events can disrupt video-based patient monitoring. Automatically detecting these periods can decrease the time required for hand-annotating recordings, which is needed for system development. Moreover, the automatic detection can be used in the future for real-time or retrospective intervention event classification. An intervention detection method based solely on depth data was developed using a vision transformer (ViT) model utilizing real-world data from patients in the NICU. Multiple design parameters were investigated, including encoding of depth data and perspective transform to account for nonoptimal camera placement. The best-performing model utilized ∼85 M trainable parameters, leveraged both perspective transform and HHA (Horizontal disparity, Height above ground, and Angle with gravity) encoding, and achieved a sensitivity of 85.6%, a precision of 89.8%, and an F1-Score of 87.6%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it