Jenny Dankelman

About

Jenny Dankelman is a prominent biomedical engineer whose research sits at the intersection of surgical robotics, minimally invasive procedures, and medical device development. Based at Delft University of Technology, she has made foundational contributions to our understanding of needle-tissue mechanics, with her 2012 survey on needle-tissue interaction forces becoming an essential reference for researchers and clinicians alike, accumulating 247 citations. Her work spans the full spectrum of interventional robotics — from developing fiber Bragg grating-based shape sensing and tracking systems for surgical needles (earning over 170 combined citations) to pioneering robotic catheter control in cardiovascular interventions. Dankelman has been instrumental in shaping European clinical practice, contributing to the 2014 EAES consensus statement on surgical robotics. In recent years, she has embraced machine learning and deep learning approaches to tackle longstanding challenges such as hysteresis compensation, autonomous endovascular navigation, and compliant motion control of robotic catheters. Her 2023 systematic review on autonomous intraluminal navigation and her 2024 machine learning survey reflect her continued leadership in defining where surgical robotics is headed. With consistently high-impact publications, Dankelman remains a defining voice in the future of safe, intelligent minimally invasive surgery.

Research Focus

Key Achievements

Top Papers

1. 1
   Needle–tissue interaction forces – A survey of experimental data

   247 citations · 2012
2. 2
   European association of endoscopic surgeons (EAES) consensus statement on the use of robotics in general surgery

   139 citations · 2014
3. 3
   Error Analysis of FBG-Based Shape Sensors for Medical Needle Tracking

   91 citations · 2013
4. 4
   Accuracy of needle position measurements using fiber Bragg gratings

   79 citations · 2012
5. 5
   Hysteresis Modeling of Robotic Catheters Based on Long Short-Term Memory Network for Improved Environment Reconstruction

   64 citations · 2021
6. 6
   Autonomous Navigation for Robot-Assisted Intraluminal and Endovascular Procedures: A Systematic Review

   58 citations · 2023
7. 7
   Shape Sensing of Flexible Robots Based on Deep Learning

   45 citations · 2022
8. 8
   Deep-Learning-Based Compliant Motion Control of a Pneumatically-Driven Robotic Catheter

   40 citations · 2022
9. 9
   Path planning for endovascular catheterization under curvature constraints via two-phase searching approach

   27 citations · 2021
10. 10
    A review on machine learning in flexible surgical and interventional robots: Where we are and where we are going

    25 citations · 2024

Key Collaborators

Contact & Links