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Atefeh Abdolmanafi, Ph.D.

Ph.D. in Computer Science with publications on Medical AI

Research Interests

Pattern recognition
Medical image analysis
Machine learning
Deep learning
Biotechnology
Atomic and Molecular Physics, and Optics
Cardiology and Cardiovascular Medicine
Biophysics
Biomedical Engineering
Health Information Management
Health Informatics
Electrical and Electronic Engineering
Computer Science Applications

About

Throughout my research journey, I have demonstrated a commitment to advancing the field of medical imaging and artificial intelligence (AI) applications in healthcare. Starting with my master's program in physics, where I specialized in optical phenomena, I built a strong foundation in imaging principles that laid the groundwork for my subsequent research endeavors. My doctoral work focused on coronary artery tissue characterization for pediatric patients with Kawasaki Disease, utilizing innovative approaches such as Convolutional Neural Networks and 3D reconstruction techniques. This work garnered international recognition, culminating in a presentation at the 12th International Symposium on Kawasaki Disease in Japan. During my postdoctoral fellowship, I led the development of a groundbreaking computer-aided diagnostic framework, addressing a critical need in healthcare and presenting at prestigious conferences. Transitioning to industry, I joined Aligo Innovation to bridge the gap between academia and industry applications, successfully contributing to technology transfer and business development. In collaboration with ViTAA Medical Solutions, I played a pivotal role in developing an automated system for analyzing computed tomography images in abdominal aortic aneurysms, resulting in filed patents and impactful publications. More recently, I have taken on a more active role in academia, mentoring students, collaborating on innovative projects, and launching the "MedTech Innovations Journal (MIJ)" to bridge technology and healthcare. Beyond my research pursuits, I am a passionate advocate for the synergy of art and science, as reflected in my book "Being Fully Connected" and recent art exhibitions in Toronto and Montreal. My multifaceted background underscores my dedication to pushing the boundaries of knowledge and creativity in the intersection of technology and healthcare.

Publications

Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography

Biomedical Optics Express / Jan 30, 2017

Abdolmanafi, A., Duong, L., Dahdah, N., & Cheriet, F. (2017). Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomedical Optics Express, 8(2), 1203. https://doi.org/10.1364/boe.8.001203

Characterization of coronary artery pathological formations from OCT imaging using deep learning

Biomedical Optics Express / Sep 21, 2018

Abdolmanafi, A., Duong, L., Dahdah, N., Adib, I. R., & Cheriet, F. (2018). Characterization of coronary artery pathological formations from OCT imaging using deep learning. Biomedical Optics Express, 9(10), 4936. https://doi.org/10.1364/boe.9.004936

An automatic diagnostic system of coronary artery lesions in Kawasaki disease using intravascular optical coherence tomography imaging

Journal of Biophotonics / Sep 02, 2019

Abdolmanafi, A., Cheriet, F., Duong, L., Ibrahim, R., & Dahdah, N. (2019). An automatic diagnostic system of coronary artery lesions in Kawasaki disease using intravascular optical coherence tomography imaging. Journal of Biophotonics, 13(1). Portico. https://doi.org/10.1002/jbio.201900112

A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography  images

Medical Physics / May 24, 2021

Abdolmanafi, A., Duong, L., Ibrahim, R., & Dahdah, N. (2021). A deep learning‐based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography  images. Medical Physics, 48(7), 3511–3524. Portico. https://doi.org/10.1002/mp.14909

Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging

Frontiers in Cardiovascular Medicine / Jan 05, 2023

Abdolmanafi, A., Forneris, A., Moore, R. D., & Di Martino, E. S. (2023). Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging. Frontiers in Cardiovascular Medicine, 9. https://doi.org/10.3389/fcvm.2022.1040053

FULLY AUTOMATIC ARTIFICIAL INTELLIGENCE DIAGNOSTIC MODEL OF CORONARY ARTERY LESIONS USING OCT IMAGING

Canadian Journal of Cardiology / Oct 01, 2019

Abdolmanafi, A., Dahdah, N., Duong, L., Adib, R. I., & Cheriet, F. (2019). FULLY AUTOMATIC ARTIFICIAL INTELLIGENCE DIAGNOSTIC MODEL OF CORONARY ARTERY LESIONS USING OCT IMAGING. Canadian Journal of Cardiology, 35(10), S61–S62. https://doi.org/10.1016/j.cjca.2019.07.468

Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling / Mar 18, 2016

Abdolmanafi, A., Prasad, A. S., Duong, L., & Dahdah, N. (2016). Classification of coronary artery tissues using optical coherence tomography imaging in Kawasaki disease. In R. J. Webster & Z. R. Yaniv (Eds.), SPIE Proceedings. SPIE. https://doi.org/10.1117/12.2216943

Intravascular imaging of coronary artery: Bridging the gap between clinical needs and technical advances

Medical Engineering & Physics / Oct 01, 2021

Abdolmanafi, A., Duong, L., Ibrahim, R., & Dahdah, N. (2021). Intravascular imaging of coronary artery: Bridging the gap between clinical needs and technical advances. Medical Engineering & Physics, 96, 71–80. https://doi.org/10.1016/j.medengphy.2021.09.003

Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features

IEEE Journal of Biomedical and Health Informatics / May 01, 2019

Abdolmanafi, A., Duong, L., Dahdah, N., & Cheriet, F. (2019). Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features. IEEE Journal of Biomedical and Health Informatics, 23(3), 931–941. https://doi.org/10.1109/jbhi.2018.2878914

Education

Ph.D., Computer Science (Artificial Intelligence) / May, 2014

Montreal, Quebec, Canada

Master of Science, Applied Physics/ Solid State Physics / January, 2009

Tehran

Bachelor of Science, Applied Physics / June, 2005

Hamedan

Experience

ViTAA Medical Solutions

Senior Research Scientist / August, 2020 — August, 2023

 Designed an automatic system to recognize and extract aortic tissues in abdominal aortic aneurysms (AAAs) using deep learning models. A patent application was filed for this study in February 2020.  Developed an automated system for pre-interventional planning and post-interventional monitoring of endovascular aortic repair using machine learning and deep learning models. A patent application was submitted for this study in October 2021.  Designed a machine learning-based model to incorporate image information into AAA growth prediction. A patent application was filed for this study in October 2022.

Université du Québec (École de technologie supérieure)

Research Assistant / June, 2019 — August, 2020

 Analyzed intraluminal coronary arteries, including the automatic detection of red thrombus, white thrombus, and residual blood.  Quantified the coronary arterial wall by automatically detecting pathological tissues, particularly calcification, macrophage accumulation, neovascularization, atheroma, lipid, and endothelial fibrosis, to prevent cardiac adverse events.

Polytechnique Montreal

Postdoctoral fellow / June, 2018 — June, 2019

 Created a computer-aided diagnostic framework that provides clinicians with operator-independent diagnoses of histological coronary lesions.  Conducted volumetric assessments of different coronary artery tissues to evaluate their dynamics and functionality.  Estimated the elasticity of coronary artery tissues affected by coronary artery disease (CAD) to assess the dynamics and functionality of the arterial wall.

Aligo Innovation

Project management and commercialization / May, 2019 — December, 2019

 Identified the optimal strategy and partners for leveraging innovative technology.  Implemented technology diffusion across various applications in the OCT market, accompanied by a licensing model tailored to market response.  Initiated collaborations with OCT system providers to achieve greater market integration.

Tehran Payam-Noor University

Adjunct professor / January, 2009 — June, 2012

 Taught undergraduate physics to over 100 students, utilizing self-developed teaching materials.  Supervised final course projects and laboratory training.  Designed field assignments and final exams.  Provided group and individual training to students for problem-solving and study skills in mathematics and physics.

Links & Social Media

Research Web Site

ORCID

ORCID
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