dr. Niki Rashidian (MD, PhD)
Postdoctoral researcher, FWO Fundamental Cancer Research
HPB Surgeon, Department of Hepatobiliary Surgery and Liver Transplantation, UZ Gent
Group Leader, Training and Research Institute for Surgical Artificial Intelligence, UGent
Board Member, E-AHPBA Innovation Committee
Principal investigator: prof. Frederik Berrevoet (MD, PhD)
Research focus
The objective of this research group is to advance the field of surgical oncology through the integration of computer sciences and artificial intelligence (AI) techniques. Their research focuses on leveraging AI algorithms and computational modeling to analyze complex cancer data, aiming to enhance surgical outcomes, improve patient care, and optimize preoperative risk stratification. By integrating cutting-edge technologies, such as computer vision and machine learning, this research team aims to discover novel predictive models and decision-support systems that facilitate accurate staging and improve personalized therapeutic strategies for patients with hepatobiliary and pancreatic cancer.
Biography
Niki Rashidian is a Hepatobiliary and Pancreatic surgeon at Ghent University Hospital. She graduated from the Medical University of Tehran and received her surgical training in Tehran, Iran. She pursued specialized fellowships in Upper GI surgery at Ghent University and Minimally Invasive HPB surgery at Catholic University of Leuven.
Dr. Rashidian obtained her PhD degree at Ghent University, focusing on leveraging computer sciences, 3D modeling, and artificial intelligence for precise preoperative planning for hepatobiliary tumors. She leads the Training and Research Institute for Surgical Artificial Intelligence (TRISAI) at Ghent University, where she conducts innovative research on computer vision and machine learning applications in surgery. Dr Rashidian is a member of the editorial board of Artificial Intelligence Surgery Journal and Acta Chirurgica Belgica.
Key publications
- Prediction Models and Risk Calculators for Post-Hepatectomy Liver Failure and Postoperative Complications using a Diverse International Cohort of Major Hepatectomies. Ann Surg. 2023 May 25. PMID: 37226846.
- Role of preoperative 3D rendering for minimally invasive parenchyma sparing liver resections. HPB (Oxford). 2023 Apr 20:S1365-182X(23)00125-9. PMID: 37149483.
- White paper: ethics and trustworthiness of artificial intelligence in clinical surgery. Artificial Intelligence Surgery. 2023;3(2):111–22
- Applications of machine learning in surgery: ethical considerations. Artificial Intelligence Surgery. 2022;2:18–23.
- Effectiveness of an immersive virtual reality environment on curricular training for complex cognitive skills in liver surgery: a multicentric crossover randomized trial. HPB (Oxford). 2022 Dec;24(12):2086-2095. PMID: 35961933.
- Using the Comprehensive Complication Index to Rethink the ISGLS Criteria for Post-hepatectomy Liver Failure in an International Cohort of Major Hepatectomies. Ann Surg. 2023 Mar 1;277(3):e592-e596. PMID: 34913896; PMCID: PMC9308484.
- Post-operative morbidity following pancreatic duct occlusion without anastomosis after pancreaticoduodenectomy: a systematic review and meta-analysis. HPB (Oxford). 2020 Aug;22(8):1092-1101. PMID: 32471694.
- Cancers Metastatic to the Liver. Surg Clin North Am. 2020 Jun;100(3):551-563. PMID: 32402300.
- Key components of a hepatobiliary surgery curriculum for general surgery residents: results of the FULCRUM International Delphi consensus. HPB (Oxford). 2020. PMID: 32060009
- Left-liver Adult-to-Adult Living Donor Liver Transplantation: Can It Be Improved? A Retrospective Multicenter European Study. Ann Surg. 2018 Nov;268(5):876-884. PMID: 30080732.
Contact & links
- Lab address: Campus UZ Gent, Ingang 12, Route 1273 Dept. of General and Hepatobiliary Surgery
- TRISAI: Training and Research Institute for Surgical Artificial Intelligence
- X (former Twitter)
- Niki Rashidian is interested to receive invitations for presentations or talks