Investigation of Machine-Learning Techniques for Pulmonary Artery Pressure Estimation from Electrical Impedance Tomography Images
Bibliographic record
Abstract
Pulmonary artery pressure (PAP) is measured for the diagnosis and monitoring of pathologies such as congestive heart failure.Currently, PAP is measured clinically using doppler ultrasound (DU) and pulmonary artery catheterization (PAC).Unfortunately, both have disadvantages which reduce their use: DU is not usable in many patients and requires trained-physician time, and PAC is invasive and increases risk.One proposal to address these issues is the measurement of PAP using a continuous and non-invasive imaging modality -electrical impedance tomography (EIT).Previous work used a model-based algorithm relying on the known association between thoracic conductivity changes and PAP.This thesis investigates improving these results by predicting PAP using neural networks (NN).Multiple NN architectures were trained and tested on a dataset of eight cardiac failure patients, and their prediction methodologies were analyzed.The resulting NN models performed well on seen patients, but failed to generalize well on unseen patients.The results present the possibility that NNs may be able to predict PAP given a more expansive dataset.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".