Prediction of rupture risk in anterior communicating artery aneurysms with a feed-forward artificial neural network
Abstract
Objectives 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.Results 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 %.Conclusion 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.Key points • 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.
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Radiological data has been combined with other data types for predictive AI systems in various clinical disciplines like oncology or neurology.
“Radiological data has been combined with other data types for predictive AI systems in various clinical disciplines like oncology21–24 or neurology25,26.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [25]
The complexity of the non-imaging data used in multimodal fusion studies was limited, with most studies using basic demographic information (age, gender), a limited range of categorical clinical history (e.g., hypertension, smoking status), or disease-specific features strongly associated with the disease of interest (e.g., APOE4 for Alzheimer's, PSA for prostate cancer).
“The complexity of the non-imaging data in multimodal fusion work was limited, particularly in the context of available feature-rich and time-series data in the EHR. Instead, most studies focused primarily on basic demographic information such as age and gender25,27,39, a limited range of categorical clinical history such as hypertension or smoking status32,34 or disease-specific clinical features”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [34]
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- Authors
- Liu, J., Chen, Y., Lan, L., Lin, B., Chen, W., Wang, M., Li, R., Yang, Y., Zhao, B., Hu, Z., & Duan, Y.
- Affiliations
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Yang, Yunjun
- Zhao, Bing
- Venue
- European Radiology
- Published
- 2018-02-23
- Volume
- 28
- Issue
- 8
- Pages
- 3268-3275
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W2792248972
- Cited by
- 107
- References
- 43
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 12 · 2022: 21 · 2021: 15 · 2020: 12 · 2019: 4 · 2018: 7 · 2017: 2 · 2016: 4 · 2015: 2 · 2014: 1
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Radiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Science and Technology Planning Projects of Wenzhou, China (Y20150289, Y20170210, Y20140733)
- Natural Science Foundation of Zhejiang Province, China (LQ15H180002)
- Science and Technology Planning Project in Medicine and Health of Zhejiang Province, China (2015KYB247)
- Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of China
- Shanghai Municipal Education Commision - Gaofeng Clinical Medicine Grant Support (20171914)
- Shanghai municipal commission of health and family planning grant (201740080)
- Project for Creative Talents of Zhejiang Province
- Topics
- Health Sciences › Medicine › Neurology › Intracranial Aneurysms: Treatment and Complications
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Aortic aneurysm repair treatments
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cerebrovascular and Carotid Artery Diseases
- Keywords
- anterior communicating artery aneurysm, rupture risk prediction, feedforward neural networks, ADASYN oversampling, CT angiography, hypertension, smoking, unruptured aneurysm
- MeSH terms
- Computed Tomography Angiography, Adult, Age Factors, Aged, Aged, 80 and over, Intracranial Aneurysm, Cerebral Angiography, Decision Making, Female, Humans, Male, Middle Aged, Models, Biological, Predictive Value of Tests, Risk Factors, ROC Curve, Neural Networks, Computer, Aneurysm, Ruptured, Anterior Cerebral Artery, Young Adult
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