Lung regions identified with CT improve the value of global inhomogeneity index measured with electrical impedance tomography
Bibliographic record
Abstract
Background: The global inhomogeneity (GI) index is a functional electrical impedance tomography (EIT) parameter which is used clinically to assess ventilation distribution. However, GI may underestimate the actual heterogeneity when the size of lung regions is underestimated. We propose a novel method to use anatomical information to correct the GI index calculation. Methods: EIT measurements were performed at the level of the fifth intercostal space in six patients with acute respiratory distress syndrome. The thorax and lungs were segmented automatically from serial individual CT scans. The anatomically derived lung regions were calculated in EIT images from simulating a homogeneous ventilation distribution in a finite element model. The conventional approach (GImeas,func), analyzes images in functionally-defined lung regions, while our proposed measure (GImeas,anat) is based on analysis in anatomically-defined regions. We additionally define a simulated comparison (GIsim,anat) to determine the lower limit of the GI measure for a homogenous distribution of ventilation. Results: As expected, the conventional GImeas,func [0.382 (0.088), median (interquartile range)] were significantly lower than the proposed GImeas,anat [0.823 (0.152), P<0.05], and were much closer to the lower limit GIsim,anat [0.343 (0.039)]. Both GImeas,anat and GImeas,func were strongly correlated with arterial oxygen partial pressure to fractional inspired oxygen ratio (R=−0.88, P<0.05), whereas GIsim,anat (R=0.23) was not. GImeas,anat had a linear-regression slope 3.2 times that of GImeas,func suggesting a higher sensitivity to the changes in lung condition. Conclusions: The proposed GImeas,anat (or shortened as GIanat) is an improved measure of ventilation inhomogeneity over GI, and better reflects portion of non-ventilated regions due to alveolar collapse or overdistension.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 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".