Towards lung EIT image segmentation: automatic classification of lung tissue state from analysis of EIT monitored recruitment manoeuvres
Bibliographic record
Abstract
There is emerging evidence that the ventilation strategy used in acute lung injury (ALI) makes a significant difference in outcome and that an inappropriate ventilation strategy may produce ventilator-associated lung injury. Most harmful during mechanical ventilation are lung overdistension and lung collapse or atelectasis. Electrical impedance tomography (EIT) as a non-invasive imaging technology may be helpful to identify lung areas at risk. Currently, no automated method is routinely available to identify lung areas that are overdistended, collapsed or ventilated appropriately. We propose a fuzzy logic-based algorithm to analyse EIT images obtained during stepwise changes of mean airway pressures during mechanical ventilation. The algorithm is tested on data from two published studies of stepwise inflation-deflation manoeuvres in an animal model of ALI using conventional and high-frequency oscillatory ventilation. The timing of lung opening and collapsing on segmented images obtained using the algorithm during an inflation-deflation manoeuvre is in agreement with well-known effects of surfactant administration and changes in shunt fraction. While the performance of the algorithm has not been verified against a gold standard, we feel that it presents an important first step in tackling this challenging and important problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".