Neidhardt, MaximilianMaximilianNeidhardtGerlach, StefanStefanGerlachLaves, Max-HeinrichMax-HeinrichLavesLatus, SarahSarahLatusStapper, CarolinCarolinStapperGromniak, MartinMartinGromniakSchlaefer, AlexanderAlexanderSchlaefer2022-01-062022-01-062021-10-09Current Directions in Biomedical Engineering 7 (2): 472-475 (2021-10-09)http://hdl.handle.net/11420/11400Needles are key tools to realize minimally invasive interventions. Physicians commonly rely on subjectively perceived insertion forces at the distal end of the needle when advancing the needle tip to the desired target. However, detecting tissue transitions at the distal end of the needle is difficult since the sensed forces are dominated by shaft forces. Disentangling insertion forces has the potential to substantially improve needle placement accuracy.We propose a collaborative system for robotic needle insertion, relaying haptic information sensed directly at the needle tip to the physician by haptic feedback through a light weight robot. We integrate optical fibers into medical needles and use optical coherence tomography to image a moving surface at the tip of the needle. Using a convolutional neural network, we estimate forces acting on the needle tip from the optical coherence tomography data. We feed back forces estimated at the needle tip for real time haptic feedback and robot control. When inserting the needle at constant velocity, the force change estimated at the tip when penetrating tissue layers is up to 94% between deep tissue layers compared to the force change at the needle handle of 2.36 %. Collaborative needle insertion results in more sensible force change at tissue transitions with haptic feedback from the tip (49.79 ± 25.51)% compared to the conventional shaft feedback (15.17 ± 15.92) %. Tissue transitions are more prominent when utilizing forces estimated at the needle tip compared to the forces at the needle shaft, indicating that a more informed advancement of the needle is possible with our system.en2364-550420212472475de Gruyterhttps://creativecommons.org/licenses/by-nc-nd/4.0/biopsyhaptic feedbackmachine learningroboticsMLE@TUHHInformatikBiowissenschaften, BiologieTechnikMedizinIngenieurwissenschaftenJournal Article10.15480/882.407910.1515/cdbme-2021-212010.15480/882.4079Journal Article