Dr. Hakeem Shakir Profile

Dr. Hakeem Shakir

at Univ of Oklahoma Health Sciences Ctr

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 12 March 2024 Paper

Developing a CT-image-based clinical marker to predict vasospasm for aneurysmal subarachnoid hemorrhage patients

Youkabed Sadri, Neman Abdoli, Ke Zhang, Patrik Gilley, Xiaochun Zhao, Kiana Prather, Hakeem Shakir, Yuchen Qiu

Proceedings Volume 12843, 1284304 (2024) https://doi.org/10.1117/12.3005015

KEYWORDS: Radiomics, Computed tomography, Deep learning, Image segmentation, Brain, Feature extraction

Read Abstract +

Vasospasm is a common complication in aneurysmal subarachnoid hemorrhage (aSAH). Currently, patients with aSAH are usually monitored at intensive care unit (ICU) for approximately 14 days for early detection and treatment of vasospasm. To facilitate the diagnosis and decision-making process, this investigation aims to combine radiomics and deep learning technologies to predict vasospasm that requires intra-arterial treatment for patients with aSAH. For this purpose, a retrospective dataset was collected, containing a total of 52 aSAH patients. Next, a total of 1032 radiomic features and 768 vision transformer (ViT) based features were computed for each case to comprehensively quantify the aSAH characteristics. Based on the initial feature pool, analysis of variance (ANOVA) F1 score was applied to select 30 best performed features as the optimal feature cluster. Finally, a support vector machine (SVM) based classifier was trained to predict the vasospasm, and the model performance was evaluated using a 4-fold cross-validation strategy. Receiver operating characteristics (ROC) curve and confusion matrix were adopted as the assessing indices. The result show that the model achieved an area under the ROC curve (AUC) of 0.86±0.03, positive predictive value of 78%, negative predictive value of 76%, and overall accuracy of 77%, respectively. This investigation initially verified the feasibility of using CT images to accurately predict cerebral vasospasm.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years