Jacqueline I. Bereska

L0.151, BMEP

Meibergdreef 9

1105 AZ Amsterdam

I’m Jackie, a PhD Candidate at the Amsterdam University Medical Centers, specializing in the application of machine learning to medical imaging and segmentation. My research aims to enhance clinical decision-making and patient outcomes through advanced AI techniques.

research

My research focuses on leveraging advanced machine learning techniques for medical imaging and segmentation, with an emphasis on improving clinical outcomes and decision-making processes. Specifically, I am engaged in:

Developing advanced radiomics and imaging biomarkers for risk assessment and treatment planning.
Utilizing AI for accurate and automated segmentation of complex tumors.
Implementing self-supervised learning architectures to enhance segmentation accuracy.
Enhancing the integration of AI solutions into clinical practice.

I am an active contributor to multidisciplinary collaborations aimed at integrating AI solutions into clinical practice, striving to bridge the gap between technological advancements and their practical application in healthcare. Additionally, I am committed to mentoring emerging researchers in the field, guiding them through projects that combine AI innovation with clinical relevance.

beyond research

When I’m not diving into the intricacies of neural networks, you might find me:

Going to the gym and practicing yoga to maintain physical and mental well-being.
Cooking vegan food and experimenting in the kitchen, creating delicious and healthy meals.
Cuddling with my two chihuahuas, Cicchetti and Pancetta.
Traveling off the beaten path, exploring unique destinations and cultures.
Learning languages, broadening my communication skills and cultural understanding.
Redecorating my house for the millionth time, much to the dismay of my husband…

selected publications

Eur Radiol Exp

Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer

Jacqueline I. Bereska, Boris V. Janssen, C. Yung Nio, Marnix P. M. Kop , and 8 more authors

Eur Radiol Exp, Feb 2024

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This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability.

@article{bereska_artificial_2024,
  title = {Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer},
  author = {Bereska, Jacqueline I. and Janssen, Boris V. and Nio, C. Yung and Kop, Marnix P. M. and Kazemier, Geert and Busch, Olivier R. and Struik, Femke and Marquering, Henk A. and Stoker, Jaap and Besselink, Marc G. and Verpalen, Inez M. and {for the Pancreatobiliary and Hepatic Artificial Intelligence Research (PHAIR) consortium}},
  year = {2024},
  month = feb,
  journal = {Eur Radiol Exp},
  volume = {8},
  number = {1},
  pages = {18},
  issn = {2509-9280},
  doi = {10.1186/s41747-023-00419-9},
  urldate = {2024-08-22},
  language = {en},
}