A safe, low-cost, easy-to-use 3D camera platform to assess risk of obstructed labor due to cephalopelvic disproportion
Bibliographic record
Abstract
Cephalopelvic disproportion (CPD)-related obstructed labor is accountable for 3-8% of the maternal deaths worldwide. The consequence of CPD-related obstructive labor in the absence of a Caesarian section (C/S) is often maternal or perinatal mortality or morbidity to the mother and/or the infant. Accurate and timely referral of at-risk mothers to health facilities where C/S is a delivery option could reduce maternal mortality in the developing world. The goal of this work was to develop and test the feasibility of a safe, low-cost, easy-to-use, portable tool, using a Microsoft Kinect 3D camera, to identify women at risk for obstructed labor due to CPD. Magnetic resonance imaging (MRI) scans, 3D camera imaging, anthropometry and clinical pelvimetry were collected and analyzed from women 18-40 years of age, at gestational age ≥36+0 weeks with previous C/S due to CPD (n = 43), previous uncomplicated vaginal deliveries (n = 96), and no previous obstetric history (n = 148) from Addis Ababa, Ethiopia. Novel and published CPD risk scores based on anthropometry, clinical pelvimetry, MRI, and Kinect measurements were compared. Significant differences were observed in most anthropometry, clinical pelvimetry, MRI and Kinect measurements between women delivering via CPD-related C/S versus those delivering vaginally. The area under the receiver-operator curve from novel CPD risk scores base on MRI-, Kinect-, and anthropometric-features outperformed novel CPD risk scores based on clinical pelvimetry and previously published indices for CPD risk calculated from these data; e.g., pelvic inlet area, height, and fetal-pelvic index. This work demonstrates the feasibility of a 3D camera-based platform for assessing CPD risk as a novel, safe, scalable approach to better predict risk of CPD in Ethiopia and warrants the need for further blinded, prospective studies to refine and validate the proposed CPD risk scores, which are required before this method can be applied clinically.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".