AI Medical Compendium Topic

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Hemangioma, Cavernous, Central Nervous System

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Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
PURPOSE: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-b...

Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familial CCMs result from autosomal dominant inheritance involving three genes: (), (), and (). CCM1 and...

Improved differentiation of cavernous malformation and acute intraparenchymal hemorrhage on CT using an AI algorithm.

Scientific reports
This study aimed to evaluate the utility of an artificial intelligence (AI) algorithm in differentiating between cerebral cavernous malformation (CCM) and acute intraparenchymal hemorrhage (AIH) on brain computed tomography (CT). A retrospective, mul...

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

Scientific reports
The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This ...