Patient-specific vascular models for endovascular and open operative procedures

Application of artificial intelligence in brain arteriovenous malformations: Angioarchitectures, clinical symptoms and prognosis prediction

BACKGROUND

Artificial intelligence (AI) has rapidly advanced in the medical field, leveraging its intelligence and automation for the management of various diseases. Brain arteriovenous malformations (AVM) are particularly noteworthy, experiencing rapid development in recent years and yielding remarkable results. This paper aims to summarize the applications of AI in the management of AVMs management.

METHODS

Literatures published in PubMed during 1999-2022, discussing AI application in AVMs management were reviewed.

RESULTS

AI algorithms have been applied in various aspects of AVM management, particularly in machine learning and deep learning models. Automatic lesion segmentation or delineation is a promising application that can be further developed and verified. Prognosis prediction using machine learning algorithms with radiomic-based analysis is another meaningful application.

CONCLUSIONS

AI has been widely used in AVMs management. This article summarizes the current research progress, limitations and future research directions.

Segmentation techniques of brain arteriovenous malformations for 3D visualization: a systematic review

BACKGROUND

Visualization, analysis and characterization of the angioarchitecture of a brain arteriovenous malformation (bAVM) present crucial steps for understanding and management of these complex lesions. Three-dimensional (3D) segmentation and 3D visualization of bAVMs play hereby a significant role. We performed a systematic review regarding currently available 3D segmentation and visualization techniques for bAVMs.

METHODS

PubMed, Embase and Google Scholar were searched to identify studies reporting 3D segmentation techniques applied to bAVM characterization. Category of input scan, segmentation (automatic, semiautomatic, manual), time needed for segmentation and 3D visualization techniques were noted.

RESULTS

Thirty-three studies were included. Thirteen (39%) used MRI as baseline imaging modality, 9 used DSA (27%), and 7 used CT (21%). Segmentation through automatic algorithms was used in 20 (61%), semiautomatic segmentation in 6 (18%), and manual segmentation in 7 (21%) studies. Median automatic segmentation time was 10 min (IQR 33), semiautomatic 25 min (IQR 73). Manual segmentation time was reported in only one study, with the mean of 5-10 min. Thirty-two (97%) studies used screens to visualize the 3D segmentations outcomes and 1 (3%) study utilized a heads-up display (HUD). Integration with mixed reality was used in 4 studies (12%).

CONCLUSIONS

A golden standard for 3D visualization of bAVMs does not exist. This review describes a tendency over time to base segmentation on algorithms trained with machine learning. Unsupervised fuzzy-based algorithms thereby stand out as potential preferred strategy. Continued efforts will be necessary to improve algorithms, integrate complete hemodynamic assessment and find innovative tools for tridimensional visualization.

Skeletonization method for vessel delineation of arteriovenous malformation

Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolization, the segmented blood vessels need to be classified into feeding arteries, draining veins and the AVM nidus. For this reason we address here the AVM localization and vessel decomposition problem. We propose in this paper a novel AVM localization and vessel delineation method using ordered thinning-based skeletonization. The main focus of vessel delineation is the delineation of draining veins since it is essential for the embolization procedure. The main contribution is a graph-based method for exact extraction of draining veins which, in combination with our earlier work on AVM detection, allows the AVM decomposition into veins, arteries and the nidus (with an emphasis on the draining veins). We validate the proposed approach on blood vessel phantoms representing the veins and the AVM structure, as well as on cerebral 3D digital rotational angiography (3DRA) images before and after embolization, paired with digital subtraction angiography (DSA) images. Results on AVM delineation show high correspondence to the ground truth structures and indicate potentials for use in surgical planning.