AIMC Topic: Hemangioma, Cavernous, Central Nervous System

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

Scientific reports May 23, 2024
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...
Brain Retrospective Studies Female Aged Tomography, X-Ray Computed Artificial Intelligence Algorithms Adolescent Humans Male Cerebral Hemorrhage Middle Aged Hemangioma, Cavernous, Central Nervous System Diagnosis, Differential Young Adult Adult
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Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning.

Scientific reports May 14, 2024
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 ...
Middle Aged ROC Curve Cerebral Hemorrhage Hemangioma, Cavernous, Central Nervous System Support Vector Machine Adult Male Machine Learning Female Humans
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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 Nov 4, 2023
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...
Hemorrhage Biomarkers Hemangioma, Cavernous, Central Nervous System Machine Learning Adult Retrospective Studies Humans Magnetic Resonance Imaging Prospective Studies Artificial Intelligence
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A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.

Neurosurgical focus Jul 1, 2025
OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to constru...
Treatment Outcome Magnetic Resonance Imaging Middle Aged Cohort Studies Retrospective Studies Adult Brain Stem Humans Young Adult Female Male Brain Stem Neoplasms Machine Learning Predictive Value of Tests Hemangioma, Cavernous, Central Nervous System Neurosurgical Procedures
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Auto-segmentation of cerebral cavernous malformations using a convolutional neural network.

BMC medical imaging May 26, 2025
BACKGROUND: This paper presents a deep learning model for the automated segmentation of cerebral cavernous malformations (CCMs).
Female Adult Humans Neural Networks, Computer Deep Learning Convolutional Neural Networks Magnetic Resonance Imaging Male Middle Aged Radiosurgery Hemangioma, Cavernous, Central Nervous System
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Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

Frontiers in bioscience (Landmark edition) Feb 21, 2024
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...
Humans Deep Learning Hemangioma, Cavernous, Central Nervous System Protein Isoforms Transcription Factors Receptors, Progesterone Carrier Proteins Proto-Oncogene Proteins
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