Semiautomatic Segmentation of Ventilated Airspaces in Healthy and Asthmatic Subjects Using Hyperpolarized<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mtext>He</mml:mtext></mml:math>MRI
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
A segmentation algorithm to isolate areas of ventilation from hyperpolarized helium-3 magnetic resonance imaging (HP (3)He MRI) is described. The algorithm was tested with HP (3)He MRI data from four healthy and six asthmatic subjects. Ventilated lung volume (VLV) measured using our semiautomated technique was compared to that obtained from manual outlining of ventilated lung regions and to standard spirometric measurements. VLVs from both approaches were highly correlated (R = 0.99; P < 0.0001) with a mean difference of 3.8 mL and 95% agreement indices of -30.8 mL and 38.4 mL. There was no significant difference between the VLVs obtained through the semiautomatic approach and the manual approach. A Dice coefficient which quantified the intersection of the two datasets was calculated and ranged from 0.95 to 0.97 with a mean of 0.96 ± 0.01 (mean ± SD). VLVs obtained through the semiautomatic algorithm were also highly correlated with measurements of forced expiratory volume in one second (FEV1) (R = 0.82; P = 0.0035) and forced vital capacity (FVC) (R = 0.95; P < 0.0001). The technique may open new pathways toward advancing more quantitative characterization of ventilation for routine clinical assessment for asthma severity as well as a number of other respiratory diseases.
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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 | 0.001 | 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