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Updated: Nov 12, 2025
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
Published on: April 13, 2013
Multidimensional predicting model of intracranial aneurysm stability with backpropagation neural network: a
Yi Yang1,2, Qingyuan Liu1,2, Pengjun Jiang1,2
1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Objectives:
The stability of intracranial aneurysms (IAs) may involve in multidimensional factors. Backpropagation (BP) neural network could be adopted to support clinical work. This preliminary study aimed to delve into the feasibility of BP neural network in assessing the risk of IA rupture/growth and to prove the advantage of multidimensional model over single/double-dimensional model.
Methods:
Thirty-six IA patients were recruited from a prospective registration study (ChiCTR1900024547). All patients were followed up until aneurysm ruptured/grew or 36 months after being diagnosed with the IAs. The multidimensional data regarding clinical, morphological, and hemodynamic characteristics were acquired. Hemodynamic analyses were conducted with patient-specific models. Based on these characteristics, seven models were built with BP neural network (the ratio of training set to validation set as 8:1). The area under curves (AUC) was calculated for subsequent comparison.
Results:
Forty-five characteristics were determined from 36 patients with 37 IAs. In the models based on the single dimension of IA characteristics, only morphological characteristics exhibited high performance in assessing 3-year IA stability (AUC = 0.703, P = 0.035). Among the models integrating two dimensions of IA characteristics, clinical-morphological (AUC = 0.731, P = 0.016), clinical-hemodynamic (AUC = 0.702, P = 0.036), and morphological-hemodynamic (AUC = 0.785, P = 0.003) models were capable of assessing the risk of 3-year IA rupture/growth. Moreover, the models including all three dimensions exhibited the maximum predicting significance (AUC = 0.811, P = 0.001).
Conclusion:
The present preliminary study reported that BP neural network might support assessing the 3-year stability of IAs. Models based on multidimensional characteristics could improve the assessment accuracy for IA rupture/growth.
