Novel Ex-vivo Calf Brain Model with Integrated Sub-Skull Force Sensors to Access Simulated Neurosurgical Procedures

Hamad Binhammad; Matheus Ballestero; Mohammed Babgi; Seana Shaka; Nima Hemati; Rothaina Saeedi; Aiden Mazidi; Bianca Giglio

cs.ROSep 13, 2026

Novel Ex-vivo Calf Brain Model with Integrated Sub-Skull Force Sensors to Access Simulated Neurosurgical Procedures

Authors: Hamad Binhammad, Matheus Ballestero, Mohammed Babgi, Seana Shaka, Nima Hemati, Rothaina Saeedi, Aiden Mazidi, Bianca Giglio,

Organizations: Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada · Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada · Department of Medicine, Federal University of Sao Carlos, Sao Carlos, Sao Paulo, Brazil · Department of Neurosurgery, Ministry of the National Guard-Health Affairs, Jeddah, Saudi Arabia; the College of Medicine, King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia; and King Abdullah International Medical Research Center, Jeddah, Saudi Arabia · Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada · Surgical Performance Enhancement and Robotics (SuPER) Centre, McGill University, Montreal, QC, Canada · Division of Pediatric Neurosurgery, Montreal Children’s Hospital, McGill University, Montreal, QC, Canada, and the Department of Neurosurgery, Ministry of National Guard Health Affairs, King Abdullah Specialized Children Hospital, Jeddah, Saudi Arabia · Dot-lab, Universite TELUQ, Montreal, QC, Canada

Abstract

Surgical tissue manipulation demands precision; however, tool-tissue manipulation force magnitudes under realistic conditions are rarely quantified. To address this gap, we proposed and validated a portable ex-vivo force-sensing platform that measures tool-tissue interaction forces across the skull-brain interface during simulated neurosurgery. The system involves fresh calf brain tissue, used as a biological surrogate for brain parenchyma, placed in a 3D-printed human skull model equipped with a 6 degree-of-freedom force/torque sensor and a real-time data acquisition system. Five validation protocols assessed the accuracy and dynamic fidelity of the platform against ground-truth measurement, static accuracy and linearity using calibrated weights (0.5-50 g), minimum detectable force, spatial consistency across different anatomical regions, effect of surgical draping, and long-duration stability. Across protocols, measured forces showed excellent agreement with reference loads (correlation R = 0.9997), with RMSE < 0.005 N and mean relative error under 2%. The platform reliably detected low-magnitude forces down to 1 g (9.8 mN), while surgical drapes introduced no meaningful signal distortion and prolonged recordings exhibited minimal drift. Overall, the proposed framework provides objective, high-fidelity force quantification for skill training and performance assessment using fresh calf brain tissue and may serve as a foundation for force-based evaluation across other surgical procedures. Future work will integrate clinically used surgical instruments to increase procedural realism and will progress toward clinical trials to evaluate usability, educational impact, and translational relevance in practice-adjacent settings.

Figures & tables

Category	Study and Year	Application / Specialty	What They Used	Performance Metric	Key Limitation
I. Instrument-mounted sensors (clinical / cadaveric)	Gan et al. , 2015 [ 3 ]	Neurosurgery, live operating room	Prototype strain-gauge force-sensing bipolar forceps	Dissection, coagulation and manipulation forces recorded intraoperatively	Pilot study; one bespoke instrument; no tissue-side reference
	Sugiyama et al. , 2017 [ 2 ]	Brain arteriovenous malformation surgery	Force-sensing bipolar forceps	Mean 0.23 $\pm$ 0.06 N vs. 0.33 $\pm$ 0.05 N (successful vs. unsuccessful)	Single pathology; forceps-specific; not used in training
	Sugiyama et al. , 2018 [ 20 ]	Neurosurgery, skill assessment	Force-sensing bipolar forceps; normative force catalogue	Novices exerted significantly greater force than experts ( $P<.001$ )	Requires sensorized forceps in the OR; tool-specific
	Albakr et al. , 2022 [ 1 ]	Hemangioblastoma surgery	SmartForceps, strain gauges on each prong	718 trials; mean force 0.20 $\pm$ 0.17 N across tasks	Tool-specific; no ground-truth calibration against known loads
	Aggravi et al. , 2016 [ 11 ]	Neurosurgery, bench model	Instrumented tool with hand- and tool-level sensors	Tool–tissue 0.01–0.59 N; surgeon–tool 0.01–6.6 N	Synthetic phantom; no biological tissue
	Maddahi et al. , 2016 [ 12 ]	Neurosurgery, cadaveric dissection	Sensorized instruments, real-time force measurement	Peak dissection forces 0.50–1.84 N	Cadaveric tissue lacks turgor; instrument modification required

TABLE I: Comparison of Existing Force-Sensing Approaches in Surgical Simulation and Skill Assessment with the Proposed Platform

Fig. 1: Overview of the experimental setup: (A) cross-sectional view of the data acquisition setup showing the distribution of loads imparted on the specimen; (B) annotated components of the experimental setup; (C) fresh ex-vivo calf brain mounted within a 3D-printed hemi-skull model with parietal (1), frontal (2), and temporal (3) anatomical locations; (D) model with surgical drapes; and (E) Kuka LBR iiwa 17 robotic arm mounted on the proposed model.

Fig. 2: Comparison of forces measured by the robot and the proposed platform in a chirp indentation test at (A) parietal, (B) frontal, and (C) temporal locations. Insets show histograms of error distributions centered around zero across all regions.

Nominal Weight (g)	Theoretical Force (N)	Mean Measured Force (N)	Mean Difference (N)
50	0.49	0.49	0.00
100	0.98	0.98	0.00
150	1.47	1.47	0.00
200	1.96	1.96	0.00
250	2.45	2.45	0.00
300	2.94	2.94	0.00

TABLE II: Protocol 1. Comparison of Robotically Applied and Subskull-Measured Forces

Nominal Weight (g)	Theoretical Force (N)	Mean Measured Force (N)	Mean Difference (N)
0	0.000	0.000	0.000
10	0.098	0.100	+0.002
20	0.196	0.199	+0.003
30	0.294	0.293	-0.001
40	0.392	0.391	-0.001
50	0.490	0.490	0.000

TABLE III: Protocol 2. Subskull Sensor Force Measurements under Incremental Loading and Unloading

Nominal Weight (g)	Theoretical Force (N)	Mean Measured Force (N)	Mean Difference (N)
0	0.000	0.000	0.000
10	0.098	0.100	+0.002
15	0.147	0.150	+0.003
17	0.167	0.170	+0.003
18	0.177	0.180	+0.003
18.5	0.182	0.180	+0.003

TABLE IV: Protocol 3A. Subskull Sensor Response under Low-Force Incremental Loading

Nominal Weight (g)	Theoretical Force (N)	Mean Measured Force (N)	Mean Difference (N)
0	0.000	0.000	0.000
1	0.0098	0.010	+0.0002
0	0.000	0.000	0.000
1	0.0098	0.010	+0.0002
0	0.000	0.000	0.000
1	0.0098	0.010	+0.0002

TABLE V: Protocol 3B. Subskull Sensor Response at 1 g Force Levels

Nominal Weight (g)	Theoretical Force (N)	Mean Measured Force (N)	Mean Difference (N)
0	0.000	0.000	0.000
10	0.098	0.099	+0.001
20	0.196	0.200	+0.004
30	0.294	0.297	+0.003
40	0.392	0.396	+0.004
50	0.490	0.490	0.000

TABLE VI: Protocol 4. Subskull Sensor Force Measurements under Incremental Loading with Surgical Drapes

Nominal Weight (g)	Theoretical Force (N)	Mean Measured Force (N)	Mean Difference (N)
0	0.000	0.000	0.000
50	0.490	0.490	0.000
0	0.000	0.000	0.000
50	0.490	0.490	0.000
0	0.000	0.000	0.000
50	0.490	0.490	0.000

TABLE VII: Protocol 5. Sub-Skull Sensor Repeatability under Cyclic Loading

Metric	Protocol 2	Protocol 3A	Protocol 4
RMSE (N)	0.0049 N	0.0028 N	0.0047 N
MRE (%)	1.70%	1.81%	1.67%
MaxAE (N)	0.0095 N	0.0034 N	0.0095 N
STD (N)	0.0028	0.0015	0.0029

TABLE VIII: Summary table of error metrics across all trials.

Metric	Protocol 2	Protocol 3A	Protocol 4
RMSE (N)	0.0049 N	0.0028 N	0.0047 N
MRE (%)	1.70%	1.81%	1.67%
MaxAE (N)	0.0095 N	0.0034 N	0.0095 N
STD (N)	0.0028	0.0015	0.0029

TABLE IX: Summary table of error metrics within sample protocols.

Fig. 3: Force measurements obtained across all experimental protocols at three anatomical locations: frontal (left column – A, D, G, J, M), parietal (middle column – B, E, H, K, N), and temporal (right column – C, F, I, L, O). For each panel, the upper plots depict the vertical force component ( $F_{z}$ ) transmitted to the brain surface, whereas the lower plots show the total force magnitude (norm of $F_{x}$ , $F_{y}$ , and $F_{z}$ ). Panels A–C correspond to Protocol 2, showing mean force profiles across six trials per location (grey traces represent the averaged response). Panels D–F correspond to Protocol 3A, also showing mean responses across six trials at each location. Panels G–I represent Protocol 3B, where a single 1 g load was sequentially applied at each location; each plot represents the mean of three trials per site. Panels J–L correspond to Protocol 4, evaluating the effect of surgical draping, with each plot representing the mean of two trials per location. Panels M–O correspond to Protocol 5, illustrating individual trial recordings during repeated loading cycles used to assess long-term signal stability and drift.

Fig. 4: Error distribution and absolute error of the measured forces in Protocols 2 (A), 3A (B), and 4 (C). The top panel shows the test period (red dots) used to compare the measured forces with the ground truth (calibrated weights). Abbreviations: RMSE, root mean square error; MAE, mean absolute error; STD, standard deviation; N, number of data points.

Fig. 5: Representative timestamps of a select group of trials with reference sampling period (red) and measured force during the trials (blue). Data in the red periods were used in the error analysis. (A) Protocol 2, parietal trial 1; (B) Protocol 4, parietal; (C) Protocol 2, parietal trial 2; (D) Protocol 3A, frontal trial 3; (E) Protocol 3A, parietal trial 1; (F) Protocol 2, frontal trial 2; (G) Protocol 3A, parietal trial 2; (H) Protocol 3A, temporal trial; and (I) Protocol 2, frontal trial 2.

Panel of Figure 15 — Fig. 6: (A) Error distribution across all trials. (B) Identity plot of measured vs. reference values (all trials). Abbreviations: N, number of data points; STD, standard deviation; Q1, first quartile; Q3, third quartile; RMSE, root mean squared error; MAE, mean absolute error.

Fig. 7: Representative force–time profiles recorded from a student, a resident and an expert neurosurgeon. Top: F norm (yellow) and its individual vector components. Bottom: Time intervals color-coded by instrument activity for metric extraction, where blue indicates microscissor manipulation and green indicates bipolar forceps use.

Hall Effect-Based Tactile Force Detection Sensor for Robot-Assisted Minimally Invasive Surgery
Oct 7, 2026Charles DeLorey, Michael Brockdorff, Adam Metcalf +3Tactile Sensing Robotic Tactile Sensing
STORM Lab, School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK
Object-Centered Reconstruction for Vision-Based 3D Force Estimation
Sep 20, 2026Zhonghao Zhang, Mingyeung Wu, Hao Yang +33D Reconstruction Surgical Robotics
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States. · Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training
Apr 30, 2026Walid Shaker, Mustafa Suphi ErdenHaptic Feedback Surgical Robotics
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK

Oct 7, 2026J/K move · Enter open · S save

Hall Effect-Based Tactile Force Detection Sensor for Robot-Assisted Minimally Invasive Surgery

Charles DeLorey, Michael Brockdorff, Adam Metcalf, Benjamin Calmé+2

STORM Lab, School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK

Tactile Sensing Robotic Tactile Sensing Surgical Robotics

Open paper AskPDF arXiv

Robotic minimally invasive surgery has revolutionized surgical practice. However, as the surgeons are mechanically separated from the surgical tools, loss of tactile feedback occurs. We present a compact force sensor with three, millimeter scale sensing elements (taxels) that can be affixed to the grasping face of a robotic surgical tool. The sensor features a miniaturized design using Hall effect sensors and magnets embedded in a deformable elastomer contact layer. Sensor calibration is achieved with a 6 degree of freedom (DoF) parallel robot. Each taxel can detect applied normal and shear forces with average RMSE of 2.133 kPa. The calibrated sensor was validated on phantom and ex vivo porcine tissue, demonstrating tactile sensing during retraction as well as slip. This work introduces a small-scale Hall effect based tactile sensor, enabling sensing for force-sensitive robotic surgical applications.

Abstract

Explore similar work

Hall Effect-Based Tactile Force Detection Sensor for Robot-Assisted Minimally Invasive Surgery

Object-Centered Reconstruction for Vision-Based 3D Force Estimation

An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training