This paper is in the following e-collection/theme issue:

Imaging Informatics (325) Artificial Intelligence (2314) Machine Learning (2212) Clinical Information and Decision Making (2050) Decision Support for Health Professionals (1623) Clinical Informatics (1421) Memory (50)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28880, first published .
Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Authors of this article:

JunHua Liao1, 2 Author Orcid Image ;   LunXin Liu1 Author Orcid Image ;   HaiHan Duan3 Author Orcid Image ;   YunZhi Huang4 Author Orcid Image ;   LiangXue Zhou1 Author Orcid Image ;   LiangYin Chen2 Author Orcid Image ;   ChaoHua Wang1 Author Orcid Image
Article Authors Cited by (10) Tweetations (2) Metrics

Journals

  1. Abdullah I, Javed A, Malik K, Malik G. DeepInfusion: A dynamic infusion based-neuro-symbolic AI model for segmentation of intracranial aneurysms. Neurocomputing 2023;551:126510 View
  2. Farhad M, Masud M, Beg A, Ahmad A, Ahmed L. A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques. Applied Sciences 2023;13(11):6582 View
  3. Shao J, Pan Y, Kou W, Feng H, Zhao Y, Zhou K, Zhong S. Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study. JMIR Medical Informatics 2024;12:e56909 View
  4. Nath-Ranga R, Absil O, Christiaens V, Garvin E. Machine learning for exoplanet detection in high-contrast spectroscopy. Astronomy & Astrophysics 2024;689:A142 View
  5. Mata-Castillo M, Hernández-Villegas A, Gordillo-Castillo N, Díaz-Román J. Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging. Medical & Biological Engineering & Computing 2025;63(9):2521 View
  6. Liu R, Zhang R, Qian W, Liang G, Chu G, Jin H, Chen L, Li J, Ma H. Intracranial aneurysm segmentation on digital subtraction angiography: a retrospective and multi-center study. Frontiers in Neurology 2025;16 View
  7. Yavuz Çelikdemir M, Akbal A. An Accurate Aneurysm Detection Model based on Artificial Intelligence. Muş Alparslan Üniversitesi Fen Bilimleri Dergisi 2025;(Advanced Online Publication):224 View
  8. Ou Y, Yan P, Liang T, Chen K, Lu B, Dai Y, Yang D, Chen Y, Cao H, Yao M, Guo J, Liang L, Li J, Nong Y, Chen J, Huang F, Zhong J, Yan Y. Multimodal therapeutic efficacy model for predicting early treatment response to TACE-HAIC combined with immune checkpoint inhibitors and tyrosine kinase inhibitors in unresectable hepatocellular carcinoma. European Journal of Radiology 2026:112784 View

Conference Proceedings

  1. Tjuatja F, Samosir A, Lucky H, Meiliana . 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). Evaluating Deep Learning Algorithms for Aneurysm Detection Using CNN and VGG-16 with Random Forest Integration View
  2. Ahmad J, Malik K, Ullah F, Ahmad K, Khan M, Malik G. 2025 Computing, Communications and IoT Applications (ComComAp). Context-Aware Detection and Grading of Intracranial Aneurysms in DSA Images View