Current aiForward participants and their projects
Robert Mozayeni MD
Foundation for the Study of Inflammatory Disease
Dr. Mozayeni is a researcher at the Foundation for the Study of Inflammatory Disease, a non-profit organization dedicated to fostering research on inflammatory processes and their diagnosis and treatment.
In the midst of the current coronavirus pandemic, the foundation and Dr. Mozayeni are focussing their efforts on studying COVID-19. More specifically, Dr. Mozayeni is investigating novel methods of detecting components of the virus in tissues and blood. The aim of this research is to develop a new diagnostic tool for more sensitive detection of COVID-19.
If you would like to learn more about how Aiforia can support researchers and organizations focussing on COVID-19 diagnostics and drug development. Contact us here.
Jan von der Thüsen MD PhD, Consultant Histopathologist
Erasmus University Medical Center, Netherlands
Dr. von der Thüsen is an experienced histopathologist at one of the largest university medical centers in Europe.
He is also a Principal Investigator in the pathology department. His current research interests are in the field of transplantation, specifically looking at the classification of solid organ rejection. Investigating rejection at a cellular level is currently conducted with mostly manual methods.
Dr. von der Thüsen applied to the aiForward program to overcome the issues of variability and sampling error often associated with these traditional image analysis evaluations. He believes that AI will not only help overcome these challenges but also improve the quality of analysis and diagnostics overall when assessing organ rejection, as he described to us: “We expect this to be a first-in-kind project.”
Polina Stepanova, PhD student
University of Helsinki, Finland
Polina is a PhD candidate at the Voutilainen Lab in the University of Helsinki’s Institute of Biotechnology.
The lab’s mission is to identify treatments for neurodegenerative diseases such as Hungtington’s disease, a fatal, inherited disorder that causes the progressive degeneration of brain cells. Polina’s PhD project is studying the effects of a promising protein at the preclinical level. Her aim is to reach clinical level usage to ultimately use it as a treatment for Hungtington’s.
Polina applied to aiForward to accelerate this translation to clinical use, as she explained to us: “Our expectations are that Aiforward can make our analysis faster and more accurate!”
Helen Remotti MD
Ladan Fazlollahi MD
Columbia University, United States
Dr. Fazlollahi and Dr. Remotti are both pathologists at New York Presbyterian Medical Center and co-Principal Investigators on a Fatty Liver Disease Project at Columbia University.
Their research focuses on alcoholic and non-alcoholic steatohepatitis, also referred to as ASH and NASH. These are liver diseases that can result in significant liver fibrosis and thus carry a high morbidity amongst patients. They aim to investigate quantitative methods for assessing fibrosis and fat from patient’s liver samples.
Pathologists traditionally make these assessments qualitatively, classifying the degrees of fat and fibrosis from mild to severe, and often only making estimates. Dr. Remotti described their motivation for applying to aiForward and having access and support to using deep learning: “AI based image analysis is an essential quantitative methodology that can serve as a more objective parameter with a higher degree of precision”.
Ilmari Parkkinen, PhD student
University of Helsinki, Finland
Ilmari is a PhD candidate at the Airavaara Lab in the University of Helsinki’s Institute of Biotechnology. The lab focuses on uncovering the mechanisms of neurodegeneration, neuroprotection and brain repair.
Parkinson’s disease, a debilitating neurodegenerative disorder, is the focus of Ilmari’s research. The aim of his PhD is to help elucidate why dopamine neurons degenerate in the disease and to find potential new ways to treat it. A big challenge in this type of research is the amount of manual and time-consuming cell counting that needs to be done.
Ilmari hopes to accelerate his research by automating these tedious tasks with deep learning AI, allowing him more time to focus on more complex problems. “We are very fortuitous to have been chosen for the program and look forward to making the most out of it,” explains Ilmari when hearing of his acceptance to aiForward.
Liam Beckett, PhD student
Jaan Korpikoski, MSc student
University of Helsinki, Finland
Liam and Jaan are both students at the Voutilainen Lab in the University of Helsinki’s Institute of Biotechnology.
The lab’s mission is to identify treatments for diseases such as amyotrophic lateral sclerosis (ALS); a fatal neurodegenerative disease. Liam and Jaan are both focusing their research on a novel protein, currently one of the most promising therapeutic candidates for ALS. The ultimate aim of this project is to take a drug candidate to clinical trial and to translate the results for the benefit of ALS patients.
Reidunn Edelmann PhD, Postdoc
University of Bergen, Norway
Dr. Edelmann is a postdoctoral researcher in Arne Östman’s laboratory at the Centre for Cancer Biomarkers, University of Bergen, Norway.
Her project aims to develop a method for tumor vessel annotation which identifies marker-defined subsets of vessels that are expected to display different functional properties. Manual annotation is time-consuming and possibly error-prone. “Our AI-based approach takes tumor vessel annotation to a new level, with the promise of a more comprehensive characterization of different vessel phenotypes. This could possibly lead to discovery of biologically meaningful and well-performing vessel-related biomarkers,” states Dr. Edelmann.
Liesbeth Hondelink, MSc student
Leiden University Medical Center, Netherlands
Liesbeth is an MSc student from Dr. Danielle Cohens’ laboratory, Leiden University Medical Center, the Netherlands.
She applied to the aiForward program to develop an algorithm for the scoring of a diagnostic marker molecule in tumor cells. According to Ms. Hondelink, the accuracy of tumor cell scoring is currently affected by inter- and intra-observer variability.
“We have already seen great and promising advances in AI-based sample image analysis,” explains Liesbeth. “In order to more adequately identify the patients that could benefit from immunotherapy, a more precise scoring method is essential.”