Multidimensional predicting model of intracranial aneurysm stability with backpropagation neural network: a preliminary study

Yang, Yi; Li, Qingyuan; Jiang, Pengjun; Yang, Junhua; Li, Maogui; Chen, Shanwen; Mo, Shaohua; Zhang, Yanan; Ma, Xuesheng; Cao, Yong; Cui, Deqi; Wu, Jun; Wang, Shuo

doi:10.1007/s10072-021-05172-8

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…The morphology assessment was performed according to our previous studies ( Liu et al, 2019 ; Chen S. et al, 2021 ; Yang et al, 2021 ). The reconstruction of vascular model and measurement of morphological parameters were conducted by two neurosurgeons (QL and YY, who were blind to clinical information and had worked as cerebral vascular neurosurgeons for more than 3 years).…”

Section: Methodsmentioning

confidence: 99%

“…The hemodynamic analysis protocol was referred to our previously conducted studies ( Liu et al, 2019 ; Chen S. et al, 2021 ; Yang et al, 2021 ). For no saccular IAs sited in A3–A5 (anterior cerebral artery), M3–M5 (middle cerebral artery), P3–P4 (posterior cerebral artery), and vertebral artery in this study, we kept the vascular from internal carotid artery to M2 and A2 for IAs sited in anterior circulation and the vascular from basilar artery to P2 for IAs sited in posterior circulation.…”

Section: Methodsmentioning

confidence: 99%

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Rebleeding of Ruptured Intracranial Aneurysm After Admission: A Multidimensional Nomogram Model to Risk Assessment

Yang

et al. 2021

Front. Aging Neurosci.

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ObjectiveRebleeding is recognized as the main cause of mortality after intracranial aneurysm rupture. Though timely intervention can prevent poor prognosis, there is no agreement on the surgical priority and choosing medical treatment for a short period after rupture. The aim of this study was to investigate the risk factors related to the rebleeding after admission and establish predicting models for better clinical decision-making.MethodsThe patients with ruptured intracranial aneurysms (RIAs) between January 2018 and September 2020 were reviewed. All patients fell to the primary and the validation cohort by January 2020. The hemodynamic parameters were determined through the computational fluid dynamics simulation. Cox regression analysis was conducted to identify the risk factors of rebleeding. Based on the independent risk factors, nomogram models were built, and their predicting accuracy was assessed by using the area under the curves (AUCs).ResultA total of 577 patients with RIAs were enrolled in this present study, 86 patients of them were identified as undergoing rebleeding after admission. Thirteen parameters were identified as significantly different between stable and rebleeding aneurysms in the primary cohort. Cox regression analysis demonstrated that six parameters, including hypertension [hazard ratio (HR), 2.54; P = 0.044], bifurcation site (HR, 1.95; P = 0.013), irregular shape (HR, 4.22; P = 0.002), aspect ratio (HR, 12.91; P < 0.001), normalized wall shear stress average (HR, 0.16; P = 0.002), and oscillatory stress index (HR, 1.14; P < 0.001) were independent risk factors related to the rebleeding after admission. Two nomograms were established, the nomogram including clinical, morphological, and hemodynamic features (CMH nomogram) had the highest predicting accuracy (AUC, 0.92), followed by the nomogram including clinical and morphological features (CM nomogram; AUC, 0.83), ELAPSS score (AUC, 0.61), and PHASES score (AUC, 0.54). The calibration curve for the probability of rebleeding showed good agreement between prediction by nomograms and actual observation. In the validation cohort, the discrimination of the CMH nomogram was superior to the other models (AUC, 0.93 vs. 0.86, 0.71 and 0.48).ConclusionWe presented two nomogram models, named CMH nomogram and CM nomogram, which could assist in identifying the RIAs with high risk of rebleeding.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Rebleeding of Ruptured Intracranial Aneurysm After Admission: A Multidimensional Nomogram Model to Risk Assessment

Yang

et al. 2021

Front. Aging Neurosci.

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“…However, studies comparing machine learning models (including SVM, RF, and ANN) with traditional statistical models and the PHASES score have confirmed the superior performance and application potential of machine learning algorithms in predicting intracranial aneurysm rupture ( 30 ). Yang et al ( 31 ) conducted a prospective study on IA rupture risk assessment using a backpropagation (BP) neural network and compared the predictive performance of BP, PHASES, UIATS, and ELAPSS scores through ROC analysis. The results showed that BP outperformed PHASES and ELAPSS scores but was slightly inferior to UIATS, which may be related to the smaller sample size included in the study.…”

Section: Applicationsmentioning

confidence: 99%

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

Wen,

Wang,

Zhong

et al. 2024

Front. Neurol.

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Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

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“…A Back-Propagation (BP) neural network can be used to evaluate the risk of IA rupture/growth, according to Yang et al [39]; thirthy-six people from a prospective registry research (ChiCTR190002447) who shared 45 features with 37 IAs were included in the study. All patients were monitored for 36 months following IA diagnosis or until aneurysm ruptured or grew.…”

Section: Ai In the Rupture Risk Assessment Of Iasmentioning

confidence: 99%

A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges

Zeng

et al. 2023

Brain Sciences

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Intracranial aneurysms (IAs) are highly prevalent in the population, and their rupture poses a significant risk of death or disability. However, the treatment of aneurysms, whether through interventional embolization or craniotomy clipping surgery, is not always safe and carries a certain proportion of morbidity and mortality. Therefore, early detection and prompt intervention of IAs with a high risk of rupture is of notable clinical significance. Moreover, accurately predicting aneurysms that are likely to remain stable can help avoid the risks and costs of over-intervention, which also has considerable social significance. Recent advances in artificial intelligence (AI) technology offer promising strategies to assist clinical trials. This review will discuss the state-of-the-art AI applications for assessing the rupture risk of IAs, with a focus on achievements, challenges, and potential opportunities.

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Multidimensional predicting model of intracranial aneurysm stability with backpropagation neural network: a preliminary study

Cited by 6 publications

References 34 publications

Rebleeding of Ruptured Intracranial Aneurysm After Admission: A Multidimensional Nomogram Model to Risk Assessment

Rebleeding of Ruptured Intracranial Aneurysm After Admission: A Multidimensional Nomogram Model to Risk Assessment

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images

A Review of Artificial Intelligence in the Rupture Risk Assessment of Intracranial Aneurysms: Applications and Challenges

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