Integrative AI and Imaging Techniques for Neurovascular Disease Assessment

AI integrated with clinical imaging equipment offers decision support to neurosurgeons in real-time, enhancing the precision of endovascular aneurysm treatments.

Unlock the potential of AI in healthcare: Join us to advance research in neurovascular disease assessment and surgical guidance using cutting-edge imaging and machine learning techniques. 

Project is Not Currently Available

This project has reached full capacity for the current term. Please check back next semester for updates.

Project description

This initiative aims to enhance the student’s understanding of neurovascular diseases and artificial intelligence (AI) applications in medical imaging. Students engaged in this project will gain proficiency in coding, specifically in Python, and learn to analyze medical imaging data, including computed tomography (CT) and magnetic resonance imaging (MRI). They will be introduced to advanced image-based machine learning techniques, such as convolutional neural networks (CNNs), and will participate in clinical meetings to understand how their work fits within the broader clinical healthcare continuum. The goal is for students to apply these skills towards assessing the severity of neurovascular diseases effectively. By the project's conclusion, they are expected to contribute to a research paper for submission to a relevant conference, enhancing their academic and professional development in the field of medical imaging and AI research. 

Project outcome

Develop and present a comprehensive research poster detailing their findings and methodologies in assessing neurovascular diseases through AI and medical imaging.
Contribute to a research paper aimed for submission to a prestigious conference, demonstrating the integration of machine learning with medical imaging for disease assessment.
Acquire hands-on experience in Python programming, data analysis, and the application of convolutional neural networks (CNNs) in interpreting complex medical imaging data. 

Project details

Timing, eligibility and other details
Length of commitment Year-long (10-12 months) 
Start time Fall (August/September)
Summer (May/June) 
In-person, remote, or hybrid? In-Person Project (can only function with in-person engagement) 
Level of collaboration Individual student project 
Benefits Stipend 
Who is eligible All undergraduate students 

Core partners

  • University at Buffalo Department of Biomedical Engineering
  • Neurosurgery Department at UB Medical School 

Project mentor

Ciprian Ionita

Assistant Professor;

Biomedical Engineering

875 Ellicott Street, 8052 Clinical Translation Research Center

Phone: (716) 400-4283

Email: cnionita@buffalo.edu

Start the project

  1. Email the project mentor using the contact information above to express your interest and get approval to work on the project. (Here are helpful tips on how to contact a project mentor.)
  2. After you receive approval from the mentor to start this project, click the button to start the digital badge. (Learn more about ELN's digital badge options.) 
Start the Digital Badge

Preparation activities

Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase. 

HIPAA Training:
Complete the online Health Insurance Portability and Accountability Act (HIPAA) training module provided by the UB Office of Compliance.
Submit a certificate of completion to ensure understanding of patient data privacy regulations.

IRB Certification:
Undergo training for the Institutional Review Board (IRB) human subjects research through the CITI Program course specific to UB.
Obtain certification that verifies knowledge of ethical research practices and regulations for working with human data. 

Keywords

Artificial Intelligence, Machine Learning, Stroke