Machine Learning Techniques for the Assessment of AAA Rupture Risk
Bibliographic record
Abstract
Recent clinical studies have shown that the maximum transverse diameter of an abdominal aortic aneurysm (AAA) alone, or in combination with its expansion rate are not entirely reliable indicators of rupture potential. We hypothesize that AAA shape, size, and wall thickness may be related to rupture risk and can be deciding factors in the clinical management of the disease. A non-invasive, image-based evaluation of AAA size and geometry was implemented using an in-house code (AAAVASC v1.0, Carnegie Mellon University) on a retrospective study of 88 subjects. The contrast enhanced, computed tomography (CT) scans of 44 patients who suffered AAA rupture within 1 month of the scan were compared to those of 44 patients who received elective repair. The images were segmented and three-dimensional models were generated. Twenty-eight geometry-based indices were calculated to characterize the size and shape of each AAA and estimate regional variations in wall thickness. A multivariate analysis of variance was performed for all indices comparing the ruptured and non-ruptured data sets to determine which indices were statistically significant. A classification model was created using a J48 decision tree algorithm and its performance was assessed using 10-fold cross validation. The model correctly classified eighty-six data sets and had an average prediction accuracy of 74% (κ = 0.69). Such a decision model can be used in a clinical setting to assess the risk of AAA rupture with minimal user intervention.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".