Rupture risk prediction of cerebral aneurysms using a novel convolutional neural network-based deep learning model

Yang, Hyeondong; Cho, Kwang‐Chun; Kim, Jung‐Jae; Kim, Jae Ho; Kim, Yong Bae; Oh, Je Hoon

doi:10.1136/neurintsurg-2021-018551

Cited by 20 publications

(11 citation statements)

References 28 publications

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“…In their study, the AUC in the CNN was 0.76, which was better than that obtained by a human evaluator (AUC: 0.54). The DLM based on hemodynamic parameters also had good predictive accuracy in assessing IA rupture risk ( 34 ). However, these studies only included anterior circulation IAs, and the results may not be applicable to other IA sites.…”

Section: Discussionmentioning

confidence: 99%

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography

Zeng,

Zhao,

Wen

et al. 2024

Quant Imaging Med Surg

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Background It is crucial to distinguish unstable from stable intracranial aneurysms (IAs) as early as possible to derive optimal clinical decision-making for further treatment or follow-up. The aim of this study was to investigate the value of a deep learning model (DLM) in identifying unstable IAs from computed tomography angiography (CTA) images and to compare its discriminatory ability with that of a conventional logistic regression model (LRM). Methods From August 2011 to May 2021, a total of 1,049 patients with 681 unstable IAs and 556 stable IAs were retrospectively analyzed. IAs were randomly divided into training (64%), internal validation (16%), and test sets (20%). Convolutional neural network (CNN) analysis and conventional logistic regression (LR) were used to predict which IAs were unstable. The area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the discriminating ability of the models. One hundred and ninety-seven patients with 229 IAs from Banan Hospital were used for external validation sets. Results The conventional LRM showed 11 unstable risk factors, including clinical and IA characteristics. The LRM had an AUC of 0.963 [95% confidence interval (CI): 0.941–0.986], a sensitivity, specificity and accuracy on the external validation set of 0.922, 0.906, and 0.913, respectively, in predicting unstable IAs. In predicting unstable IAs, the DLM had an AUC of 0.771 (95% CI: 0.582–0.960), a sensitivity, specificity and accuracy on the external validation set of 0.694, 0.929, and 0.782, respectively. Conclusions The CNN-based DLM applied to CTA images did not outperform the conventional LRM in predicting unstable IAs. The patient clinical and IA morphological parameters remain critical factors for ensuring IA stability. Further studies are needed to enhance the diagnostic accuracy.

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Section: Discussionmentioning

confidence: 99%

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography

Zeng,

Zhao,

Wen

et al. 2024

Quant Imaging Med Surg

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“…34 Nevertheless, several hemodynamic studies have used FSI analysis. [35][36][37] Cho et al 35 suggested in FSI analysis that strain was more effective than WSS in predicting the rupture risk of cerebral aneurysms. In addition, unlike WSS, which shows inconsistency as it pregresses from formation to growth and rupture, high strain continues to be involved in the natural history of cerebral aneurysms.…”

Section: Another Hemodynamic Studymentioning

confidence: 99%

The Current Limitations and Advanced Analysis of Hemodynamic Study of Cerebral Aneurysms

Cho

2023

Neurointervention

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Among the various perspectives on cerebrovascular diseases, hemodynamic analysis—which has recently garnered interest—is of great help in understanding cerebrovascular diseases. Computational fluid dynamics (CFD) analysis has been the primary hemodynamic analysis method, and studies on cerebral aneurysms have been actively conducted. However, owing to the intrinsic limitations of the analysis method, the role of wall shear stress (WSS), the most representative parameter, remains controversial. High WSS affects the formation of cerebral aneurysms; however, no consensus has been reached on the role of WSS in the growth and rupture of cerebral aneurysms. Therefore, this review aimed to briefly introduce the up-to-date results and limitations made through CFD analysis and to inform the need for a new hemodynamic analysis method.

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“…The deep shape learning method could achieve the highest accuracy of 0.82. Yang et al ( 20 ) utilized CNN on hemodynamic factors of WSS and strain to predict the rupture risk of cerebral aneurysms, and the best AUC was up to 0.883.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs

Chen

et al. 2022

Front. Neurol.

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BackgroundThe rupture risk assessment of intracranial aneurysms (IAs) is clinically relevant. How to accurately assess the rupture risk of IAs remains a challenge in clinical decision-making.PurposeWe aim to build an integrated model to improve the assessment of the rupture risk of IAs.Materials and MethodsA total of 148 (39 ruptured and 109 unruptured) IA subjects were retrospectively computed with computational fluid dynamics (CFDs), and the integrated models were proposed by combining machine learning (ML) and deep learning (DL) algorithms. ML algorithms that include random forest (RF), k-nearest neighbor (KNN), XGBoost (XGB), support vector machine (SVM), and LightGBM were, respectively, adopted to classify ruptured and unruptured IAs. A Pointnet DL algorithm was applied to extract hemodynamic cloud features from the hemodynamic clouds obtained from CFD. Morphological variables and hemodynamic parameters along with the extracted hemodynamic cloud features were acted as the inputs to the classification models. The classification results with and without hemodynamic cloud features are computed and compared.ResultsWithout consideration of hemodynamic cloud features, the classification accuracy of RF, KNN, XGB, SVM, and LightGBM was 0.824, 0.759, 0.839, 0.860, and 0.829, respectively, and the AUCs of them were 0.897, 0.584, 0.892, 0.925, and 0.890, respectively. With the consideration of hemodynamic cloud features, the accuracy successively increased to 0.908, 0.873, 0.900, 0.926, and 0.917. Meanwhile, the AUCs reached 0.952, 0.881, 0.950, 0.969, and 0.965 eventually. Adding consideration of hemodynamic cloud features, the SVM could perform best with the highest accuracy of 0.926 and AUC of 0.969, respectively.ConclusionThe integrated model combining ML and DL algorithms could improve the classification of IAs. Adding consideration of hemodynamic cloud features could bring more accurate classification, and hemodynamic cloud features were important for the discrimination of ruptured IAs.

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Rupture risk prediction of cerebral aneurysms using a novel convolutional neural network-based deep learning model

Cited by 20 publications

References 28 publications

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography

Compare deep learning model and conventional logistic regression model for the identification of unstable saccular intracranial aneurysms in computed tomography angiography

The Current Limitations and Advanced Analysis of Hemodynamic Study of Cerebral Aneurysms

An Integrated Model Combining Machine Learning and Deep Learning Algorithms for Classification of Rupture Status of IAs

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