Prédire la transformation hémorragique symptomatique à la suite d'un infarctus cérébral : introduction à une approche clinico-radiologique en machine learning et basée sur l'IRM
Bibliographic record
Abstract
Ischemic stroke is a leading cause of death and disability in adult. Reperfusion therapies using intravenous thrombolysis (IVT) and endovascular interventions such as mechanical thrombectomy (MT) improve functional outcomes in patients with acute ischemic stroke (AIS) but unfortunately, these therapeutics increase the risk of intracranial haemorrhage (ICH) of ischemic brain tissue. Several definitions and classifications exist to define this complication, on a clinical or radiological level. However, high evidence indicates that symptomatic ICH (sICH) is the most relevant definition as it is most correlated with poor outcome. Several methods, based on clinical and/or radiological data, failed to predict the risk of haemorrhagic transformation in clinical practice. We attempted a new approach by training a supervised machine learning (ML) algorithm on clinical and MRI data within a 100 subjects cohort of patients with anterior circulation AIS treated by IVT and/or MT who underwent sICH (n=28), non-symptomatic ICH (n=27) and 45 controls with no bleeding, matched on clinical severity and age. We compared ML algorithm accuracy to the performance of the clinical Totaled Health Risks in Vascular Events (THRIVE) score and to the radiological Alberta Stroke Program Early CT Score applied to MR imaging (DWI-ASPECTS). ML algorithm predicted sICH with an Area Under receiver operating characteristic Curve (AUC) of 0.658 (CI 95% [0.534 – 0.783]). Applied in the cohort, estimated AUC of THRIVE score and DWI-ASPECTS were 0.664 (CI 95% [0.548 – 0.781]) and 0.634 (IC 95% [0.508 – 0.761]), respectively. Although it do not outperform current tools, this work showed that this algorithm was able to synthesize all clinical and radiological data provided and integrating the variety of information provided by MR imaging to provide a probability of sICH. Further studies are needed to improve these performances. Enlarging dataset is needed to improve learning phase, reduce overfitting risk and allow a 3D analysis to avoid data loss. More relevant clinical and radiological data could also be integrated to improve performances. Validation of these results on a more heterogeneous external population is also required.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".