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A machine learning approach to MRI screening for brain arteriovenous malformations in hereditary hemorrhagic telangiectasia
- Truvace record
- TRV-2026-2476raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Observational study
- Sources
- 1 sourcePeer-reviewed
- sha256
- 1dc16315a786…check it
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…
The gain
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).
What this doesn’t fix
The bAVM tool has not been externally validated, and the models for any cerebrovascular malformation showed limited discrimination and poor calibration.
Sources
1 on file- Neuroradiologydoi:10.1007/s00234-026-04253-9
Cite this record
v1Truvace Impact Record TRV-2026-2476, v1: “A machine learning approach to MRI screening for brain arteriovenous malformations in hereditary hemorrhagic telangiectasia.” Truvace, 2026-11-12. /record/TRV-2026-2476 (accessed on the day you copy it). sha256 1dc16315a7867882…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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