Literature DB >> 32165361

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives.

Z Shi1, B Hu1, U J Schoepf2, R H Savage2, D M Dargis2, C W Pan3, X L Li3,4, Q Q Ni1, G M Lu1, L J Zhang5.   

Abstract

Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computer-assisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed.
© 2020 by American Journal of Neuroradiology.

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Year:  2020        PMID: 32165361      PMCID: PMC7077887          DOI: 10.3174/ajnr.A6468

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  54 in total

1.  Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography.

Authors:  Joseph N Stember; Peter Chang; Danielle M Stember; Michael Liu; Jack Grinband; Christopher G Filippi; Philip Meyers; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

2.  Prospective Assessment of a Symptomatic Cerebral Vasospasm Predictive Neural Network Model.

Authors:  Travis M Dumont
Journal:  World Neurosurg       Date:  2016-07-05       Impact factor: 2.104

3.  Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA.

Authors:  T Sichtermann; A Faron; R Sijben; N Teichert; J Freiherr; M Wiesmann
Journal:  AJNR Am J Neuroradiol       Date:  2018-12-20       Impact factor: 3.825

4.  Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years: a cross-sectional study.

Authors:  Ming-Hua Li; Shi-Wen Chen; Yong-Dong Li; Yuan-Chang Chen; Ying-Sheng Cheng; Ding-Jun Hu; Hua-Qiao Tan; Qian Wu; Wu Wang; Zhen-Kui Sun; Xiao-Er Wei; Jia-Yin Zhang; Rui-Hua Qiao; Wen-Hong Zong; Yin Zhang; Wei Lou; Zhi-Yuan Chen; Yu Zhu; De-Rong Peng; Sui-Xin Ding; Xue-Fan Xu; Xu-Hong Hou; Wei-Ping Jia
Journal:  Ann Intern Med       Date:  2013-10-15       Impact factor: 25.391

5.  Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission.

Authors:  Christian Rubbert; Kaustubh R Patil; Kerim Beseoglu; Christian Mathys; Rebecca May; Marius G Kaschner; Benjamin Sigl; Nikolas A Teichert; Johannes Boos; Bernd Turowski; Julian Caspers
Journal:  Eur Radiol       Date:  2018-06-12       Impact factor: 5.315

6.  Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features.

Authors:  QingLin Liu; Peng Jiang; YuHua Jiang; HuiJian Ge; ShaoLin Li; HengWei Jin; YouXiang Li
Journal:  Stroke       Date:  2019-07-10       Impact factor: 7.914

7.  Interobserver variability of aneurysm morphology: discrimination of the daughter sac.

Authors:  Sang Hyun Suh; Harry J Cloft; John Huston; Kyung Hwa Han; David F Kallmes
Journal:  J Neurointerv Surg       Date:  2014-11-10       Impact factor: 5.836

8.  Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Authors:  Nikhil Paliwal; Prakhar Jaiswal; Vincent M Tutino; Hussain Shallwani; Jason M Davies; Adnan H Siddiqui; Rahul Rai; Hui Meng
Journal:  Neurosurg Focus       Date:  2018-11-01       Impact factor: 4.047

9.  An ellipsoid convex enhancement filter for detection of asymptomatic intracranial aneurysm candidates in CAD frameworks.

Authors:  Ze Jin; Hidetaka Arimura; Shingo Kakeda; Fumio Yamashita; Makoto Sasaki; Yukunori Korogi
Journal:  Med Phys       Date:  2016-02       Impact factor: 4.071

10.  Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.

Authors:  Mohammad Hesam Hesamian; Wenjing Jia; Xiangjian He; Paul Kennedy
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

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  9 in total

Review 1.  Machine Learning Algorithms in Neuroimaging: An Overview.

Authors:  Vittorio Stumpo; Julius M Kernbach; Christiaan H B van Niftrik; Martina Sebök; Jorn Fierstra; Luca Regli; Carlo Serra; Victor E Staartjes
Journal:  Acta Neurochir Suppl       Date:  2022

2.  Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.

Authors:  Vittorio Stumpo; Victor E Staartjes; Giuseppe Esposito; Carlo Serra; Luca Regli; Alessandro Olivi; Carmelo Lucio Sturiale
Journal:  Acta Neurochir Suppl       Date:  2022

3.  Automated Aneurysm Detection: Emerging from the Shallow End of the Deep Learning Pool.

Authors:  David F Kallmes; Bradley J Erickson
Journal:  Radiology       Date:  2020-11-03       Impact factor: 11.105

4.  Development and Validation of an Automatic System for Intracerebral Hemorrhage Medical Text Recognition and Treatment Plan Output.

Authors:  Bo Deng; Wenwen Zhu; Xiaochuan Sun; Yanfeng Xie; Wei Dan; Yan Zhan; Yulong Xia; Xinyi Liang; Jie Li; Quanhong Shi; Li Jiang
Journal:  Front Aging Neurosci       Date:  2022-04-08       Impact factor: 5.702

5.  CORR Insights®: Lower Success Rate of Débridement and Implant Retention in Late Acute versus Early Acute Periprosthetic Joint Infection Caused by Staphylococcus spp. Results from a Matched Cohort Study.

Authors:  Jacob M Drew
Journal:  Clin Orthop Relat Res       Date:  2020-06       Impact factor: 4.755

6.  A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Authors:  Zhao Shi; Chongchang Miao; U Joseph Schoepf; Rock H Savage; Danielle M Dargis; Chengwei Pan; Xue Chai; Xiu Li Li; Shuang Xia; Xin Zhang; Yan Gu; Yonggang Zhang; Bin Hu; Wenda Xu; Changsheng Zhou; Song Luo; Hao Wang; Li Mao; Kongming Liang; Lili Wen; Longjiang Zhou; Yizhou Yu; Guang Ming Lu; Long Jiang Zhang
Journal:  Nat Commun       Date:  2020-11-30       Impact factor: 14.919

7.  A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.

Authors:  Bio Joo; Hyun Seok Choi; Sung Soo Ahn; Jihoon Cha; So Yeon Won; Beomseok Sohn; Hwiyoung Kim; Kyunghwa Han; Hwa Pyung Kim; Jong Mun Choi; Sang Min Lee; Tae Gyu Kim; Seung-Koo Lee
Journal:  Yonsei Med J       Date:  2021-11       Impact factor: 2.759

8.  Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion.

Authors:  Xingwei An; Jiaqian He; Yang Di; Miao Wang; Bin Luo; Ying Huang; Dong Ming
Journal:  Front Neurosci       Date:  2022-02-17       Impact factor: 4.677

9.  Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia.

Authors:  Masahito Katsuki; Shin Kawamura; Akihito Koh
Journal:  Cureus       Date:  2021-06-16
  9 in total

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