Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms

Varble, Nicole; Tutino, Vincent M.; Yu, Jihnhee; Sonig, Ashish; Siddiqui, Adnan H.; Davies, Jason M

doi:10.1161/strokeaha.117.019929

Cited by 63 publications

(41 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, aneurysm size remains a major criterion for treatment as it is assumed that large IAs are at higher risk for rupture than small IAs. However, small IAs with regular shape may also rupture . Therefore, it has been suggested that growing aneurysms independently of their size are more likely to rupture .…”

Section: Introductionmentioning

confidence: 99%

“…Hence, more factors signifying the biological processes leading to growth and rupture should be considered for decisions onto treatment of unruptured IAs. Indeed, there is increasing evidence showing that hemodynamic forces exerted on the vessel wall by the flowing blood may induce vascular remodelling leading to IA formation, growth and rupture . Hemodynamic forces may thus be considered as additional prediction factors for IA outcome .…”

Section: Introductionmentioning

confidence: 99%

“…However, small IAs with regular shape may also rupture. 10 Therefore, it has been suggested that growing aneurysms independently of their size are more likely to rupture. 11,12 Hence, more factors signifying the biological processes leading to growth and rupture should be considered for decisions onto treatment of unruptured IAs.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, there is increasing evidence showing that hemodynamic forces exerted on the vessel wall by the flowing blood may induce vascular remodelling leading to IA formation, growth and rupture. 10,[13][14][15][16][17][18][19][20] Hemodynamic forces may thus be considered as additional prediction factors for IA outcome. 19,21 In this review, we will focus on the influence of hemodynamic forces on IA pathogenesis as well as on mechanoreceptors and intracellular signalling pathways that mediate vascular wall remodelling.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Role of hemodynamics in initiation/growth of intracranial aneurysms

Diagbouga

Morel

Bijlenga

et al. 2018

Eur J Clin Investigation

View full text Add to dashboard Cite

Background: Intracranial aneurysm (IA) is a disease of the vascular wall resulting in abnormal enlargement of the vessel lumen. It is a common pathology with a prevalence of 2%-3% in the adult population. IAs are mostly small, quiescent and asymptomatic; yet, upon rupture, severe brain damage or even death is frequently encountered. In addition to clinical factors, hemodynamic forces, mainly wall shear stress (WSS), have been associated with the initiation of IAs and possibly with their risk of rupture. However, the mechanism by which WSS contributes to aneurysm growth and rupture is not completely understood. Design: PubMed and Ovid MEDLINE databases were searched. In addition, key review articles were screened for relevant original publications. Results: Current knowledge about the relation between WSS and IA has been obtained from both computational fluid dynamic studies in patients and experimental models of IA formation and growth. It is increasingly recognized that a high wall shear stress (gradient) participates to IA formation and that both low and high WSS can drive IA growth. Primary cilia (PC) play an important role as mechanosensors as patients with polycystic kidney disease, which is characterized by the absence or dysfunction of PC, have increased risk to develop IAs as well as increased risk of rupture. Conclusion: Wall shear stress is a key player in IA initiation and progression. It is involved in vascular wall remodelling and inflammation, processes underlying aneurysm pathophysiology. K E Y W O R D Sendothelium, intracranial aneurysms, primary cilium, wall shear stress

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Role of hemodynamics in initiation/growth of intracranial aneurysms

Diagbouga

Morel

Bijlenga

et al. 2018

Eur J Clin Investigation

View full text Add to dashboard Cite

show abstract

“…These algorithms have been successfully used in AI studies. 11,12 Compared with traditional ML algorithms, DL uses an artificial neural network that extracts features from images automatically to create its own filters, called feature maps (independent of human input) and memorizes visual patterns with highest frequency. There are convolution layers, pooling layers, fully connected layers, and normalization layers; the pooling reduces the number of parameters and reduces overfitting.…”

Section: Brief Overview Of Aimentioning

confidence: 99%

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Shi

Schoepf

et al. 2020

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computerassisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed. ABBREVIATIONS: AI¼ artificial intelligence; AUC ¼ area under the curve; CAD ¼ computer-assisted diagnostics; DL ¼ deep learning; FP ¼ false-positive; ML ¼ machine learning; SVM ¼ support vector machines

show abstract

A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images

Qian

Chong

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Background Intracranial aneurysms (IAs) are a life‐threatening disease. Their rupture can lead to hemorrhagic stroke. Most studies applying deep learning for the detection of aneurysms are based on angiographic images. However, critical diagnostic information such as morphology and aneurysm location are not captured by deep learning algorithms and still require manual assessments. Purpose Digital subtraction angiography (DSA) is the gold standard for aneurysm diagnosis. To facilitate the fully automatic diagnosis of aneurysms, we proposed a comprehensive system for the detection, morphology measurement, and location classification of aneurysms on three‐dimensional DSA images, allowing automatic diagnosis without further human input. Methods The system comprised three neural networks: a network for aneurysm detection, a network for morphology measurement, and a network for aneurysm location identification. A cross‐scale dual‐path transformer module was proposed to effectively fuse local and global information to capture aneurysms of varying sizes. A multitask learning approach was also proposed to allow an accurate localization of aneurysm neck for morphology measurement. Results The cross‐scale dual‐path transformer module was shown to outperform other state‐of‐the‐art network architectures, improving segmentation, and classification accuracy. The detection network in our system achieved an F2 score of 0.946 (recall 93%, precision 100%), better than the winning team in the Cerebral Aneurysm Detection and Analysis challenge. The measurement network achieved a relative error of less than 10% for morphology measurement, at the same level as human operators. Perfect accuracy (100%) was achieved on aneurysm location classification. Conclusions We have demonstrated that a comprehensive system can automatically detect, measure morphology and report the aneurysm location of aneurysms without human intervention. This can be a potential tool for the diagnosis of IAs, improving radiologists’ performance and reducing their workload.

show abstract

Shared and Distinct Rupture Discriminants of Small and Large Intracranial Aneurysms

Abstract: Small (<5 mm) and large (≥5 mm) IAs have different hemodynamic and clinical, but not morphological, rupture discriminants. Size-dichotomized rupture discrimination models performed better than the aggregate model.

Cited by 63 publications

References 33 publications

Role of hemodynamics in initiation/growth of intracranial aneurysms

Role of hemodynamics in initiation/growth of intracranial aneurysms

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

A deep learning–based automatic system for intracranial aneurysms diagnosis on three‐dimensional digital subtraction angiographic images

Contact Info

Product

Resources

About