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TRUVACE RECORD VERSION
record: TRV-2026-2476
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T00:01:55.159248Z
status: published
lens: g_space
sector: health
headline: A machine learning approach to MRI screening for brain arteriovenous malformations in hereditary hemorrhagic telangiectasia
dek: Background Hereditary hemorrhagic telangiectasia (HHT) is an inherited vascular disorder characterized by telangiectasias and arteriovenous malformations (AVMs) in multiple organs. Although brain MRI screening is recommended in all patients with HHT, in many centers, this may be difficult to implement due to organizational constraints. The aim of this study was to develop a machine learning model to predict the presence of bAVMs and other cerebrovascular malformations in patients with HHT, with the goal of suppo…
gain_title: In a study of 142 patients with hereditary hemorrhagic telangiectasia, machine learning models predicted brain arteriovenous malformations with an AUC of 0.84 (logistic regression) and 0.85 (random forest).
problem_title: (none)
trace_subject: (none)
gain_reading: In a study of 142 patients with hereditary hemorrhagic telangiectasia, machine learning models predicted brain arteriovenous malformations with an AUC of 0.84 (logistic regression) and 0.85 (random forest).
gain_evidence: both models showed good discrimination, with an AUC of 0.84 (95% CI: 0.73-0.94) for Logistic Regression (LR) and 0.85 (95% CI: 0.74-0.96) for Random Forest (RF)
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built two machine learning models, logistic regression and random forest, to predict brain arteriovenous malformations (bAVMs) in 142 patients with confirmed hereditary hemorrhagic telangiectasia. Both reached good discrimination, with an AUC of 0.84 for logistic regression and 0.85 for random forest.

Brain MRI screening is recommended for all HHT patients, but many centers find it hard to carry out because of organizational constraints. The authors aim to support risk stratification in centers that already hold genotype information and to identify patients who may suit closer surveillance.

The broader model did not hold up: those predicting any cerebrovascular malformation showed limited discrimination and poor calibration. The authors say the bAVM tool needs external validation before it could guide how screening is allocated.
limitation: The bAVM tool has not been externally validated, and the models for any cerebrovascular malformation showed limited discrimination and poor calibration.
tag: Evidence-backed gain
key_points: The study included 142 patients with confirmed HHT who met at least 3 Curaçao criteria and had complete genetic work-up and radiological screening between 2018 and 2026. | Logistic regression reached an AUC of 0.84 (95% CI 0.73-0.94) for bAVM prediction; random forest reached 0.85 (95% CI 0.74-0.96). | Random forest had higher sensitivity (0.73 vs 0.69) but lower specificity, PPV and F1 score, with the same NPV. | Logistic regression was better calibrated, with an intercept of -0.30 and slope of 0.87, versus 1.39 and 2.39 for random forest. | Models for any cerebrovascular malformation showed limited discrimination and poor calibration. | Top logistic regression features were deletion mutations, ENG, family history, ACVRL1 and truncated variants.
rundown: The team defined two end points: the probability of detecting bAVMs and the probability of detecting any cerebrovascular finding. Analysis used Python and R, and patients were drawn from those with complete genetic and radiological data between 2018 and 2026.

For bAVM prediction the two models differed in trade-offs. Random forest had a slightly higher AUC and sensitivity (0.73 vs 0.69), while logistic regression had better specificity, PPV, F1 score and calibration.

Feature analysis pointed to genotype and family history. Logistic regression ranked deletion mutations, ENG, family history, ACVRL1 and truncated variants highest, while random forest SHAP analysis ranked ACVRL1, ENG, age, pulmonary AVMs and family history.

The authors conclude the phenotype-specific bAVM model achieved effective risk stratification, and that after external validation it may allow more efficient allocation of screening strategies.
sources:
- peer_reviewed | Neuroradiology | https://doi.org/10.1007/s00234-026-04253-9 | 2026-11-07
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