Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network
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Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN).
Materials and method
594 ACOM aneurysms, 54 unruptured and 540 ruptured, were reviewed. A two-layer feed-forward ANN was designed for ACOM aneurysm rupture-risk analysis. To improve ANN efficiency, an adaptive synthetic (ADASYN) sampling approach was applied to generate more synthetic data for unruptured aneurysms. Seventeen parameters (13 morphological parameters of ACOM aneurysm measured from these patients' CT angiography (CTA) images, two demographic factors, and hypertension and smoking histories) were adopted as ANN input.
Age, vessel size, aneurysm height, perpendicular height, aneurysm neck size, aspect ratio, size ratio, aneurysm angle, vessel angle, aneurysm projection, A1 segment configuration, aneurysm lobulations and hypertension were significantly different between the ruptured and unruptured groups. Areas under the ROC curve for training, validating, testing and overall data sets were 0.953, 0.937, 0.928 and 0.950, respectively. Overall prediction accuracy for raw 594 samples was 94.8 %.
This ANN presents good performance and offers a valuable tool for prediction of rupture risk in ACOM aneurysms, which may facilitate management of unruptured ACOM aneurysms.
• A feed-forward ANN was designed for the prediction of rupture risk in ACOM aneurysms.
• Two demographic parameters, 13 morphological aneurysm parameters, and hypertension/smoking history were acquired.
• An ADASYN sampling approach was used to improve ANN quality.
• Overall prediction accuracy of 94.8 % for the raw samples was achieved.
KeywordsAneurysm Angiography Machine learning Risk Rupture
Anterior communicating artery
Artificial neural network
Computational fluid dynamics
Computed tomography angiography
Digital subtraction angiography
Receiver operating characteristic curve
This study received funding by Science and Technology Planning Project in Medicine and Health of Zhejiang Province, China (Grant No. 2015KYB247), Natural Science Foundation of Zhejiang Province, China (Grant No. LQ15H180002), Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of China, Science and Technology Planning Projects of Wenzhou, China (Grant No. Y20150289, Y20170210 and Y20140733), Shanghai Municipal Education Commision - Gaofeng Clinical Medicine Grant Support (20171914) Shanghai municipal commission of health and family planning grant (201740080), and Project for Creative Talents of Zhejiang Province.
Compliance with ethical standards
The scientific guarantor of this publication is Yunjun Yang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
Study subjects or cohorts overlap
Some study subjects or cohorts have been previously reported in ‘Lin, B., Chen, W., Ruan, L., Chen, Y., Zhong, M., Zhuge, Q., ... & Yang, Y. (2016). Sex differences in aneurysm morphologies and clinical outcomes in ruptured anterior communicating artery aneurysms: a retrospective study. BMJ Open, 6(4), e009920.’ and ‘Xia, N., Liu, Y., Zhong, M., Zhuge, Q., Fan, L., Chen, W., ... & Zhao, B. (2016). Smoking associated with increased aneurysm sizein patients with anterior communicating artery aneurysms. World Neurosurg, 87, 155-161.’
• diagnostic or prognostic study
• performed at one institution
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