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, Rukun Dou, Houssem-Eddine Gueziri, Amir Hooshiar, Rolando F. Del Maestro
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
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)
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
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 ( Fz ) transmitted to the brain surface, whereas the lower plots show the total force magnitude (norm of Fx , Fy , and Fz ). 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.
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.
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.
Charles DeLorey, Michael Brockdorff, Adam Metcalf +3
STORM Lab, School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK
Excessive force may damage tissue and increase the risk of anastomotic leakage in robotic colorectal surgery. Although the da Vinci 5 provides force sensing, this capability is unavailable on earlier da Vinci systems and many other surgical robotic platforms. In this work, we present a vision-based pipeline for estimating 3D interaction forces from soft-tissue deformation in stereo endoscopic video. We dynamically reconstruct the tissue point cloud in an object-centered coordinate frame, track tissue points with geometric constraints, and predict the 3D force vector with a neural network. We progressively evaluate the pipeline on rubber-glove phantoms, ex vivo porcine colons, and in vivo colorectal surgical video sequences. Under varying tissue orientations and positions within the endoscopic view, as well as different camera viewpoints, the proposed method achieves average root mean square error (RMSEs) of 0.77 N and 1.30 N on the phantom and porcine colon, respectively. Compared with the camera-frame representation, the object-centered representation reduces average RMSE by 51.3% and 56.7%, while geometry-constrained tracking reduces RMSE by 19.8% and 25.3% compared with CoTracker. We further qualitatively demonstrate the feasibility of vision-based force estimation on an in vivo colorectal surgical sequence, as a step toward clinical translation of vision-based, sensorless force estimation.
Zhonghao Zhang, Mingyeung Wu, Hao Yang +3
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States. · Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
Robotic-assisted surgery offers significant clinical advantages but largely eliminates direct haptic feedback, increasing the risk of excessive tool-tissue interaction forces. Although recent commercial systems have begun to introduce force feedback, their high cost limits accessibility, particularly for surgical training. This paper presents a modular experimental robotic laparoscopic instrument integrated with a real-time haptic feedback framework. The proposed instrument employs a wrist-mounted force/torque (F/T) sensor to estimate tool-tissue interaction forces while avoiding the durability and integration challenges of tip-mounted sensors. A haptic feedback framework is developed to extract the external contact forces, render them to the haptic device, and generate stable and perceptually meaningful feedback. The instrument is integrated into the robotic surgery training system (RoboScope) and evaluated through a controlled user study involving a force regulation task. Experimental results demonstrate that haptic feedback significantly improves task success rate, force regulation accuracy, and task efficiency compared to visual-only feedback. The proposed instrument enables stable, high-fidelity haptic interaction, supporting effective robotic surgery training.
Walid Shaker, Mustafa Suphi Erden
School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK