Computational Analyses of Arteriovenous Malformations in Neuroimaging
Bibliographic record
Abstract
Computational models have been investigated for the analysis of the physiopathology and morphology of arteriovenous malformation (AVM) in recent years. Special emphasis has been given to image fusion in multimodal imaging and 3-dimensional rendering of the AVM, with the aim to improve the visualization of the lesion (for diagnostic purposes) and the selection of the nidus (for therapeutic aims, like the selection of the region of interest for the gamma knife radiosurgery plan). Searching for new diagnostic and prognostic neuroimaging biomarkers, fractal-based computational models have been proposed for describing and quantifying the angioarchitecture of the nidus. Computational modeling in the AVM field offers promising tools of analysis and requires a strict collaboration among neurosurgeons, neuroradiologists, clinicians, computer scientists, and engineers. We present here some updated state-of-the-art exemplary cases in the field, focusing on recent neuroimaging computational modeling with clinical relevance, which might offer useful clinical tools for the management of AVMs in the future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".