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

• URL: http://arxiv.org/abs/2609.14198v1
• Date: Sun, 13 Sep 2026 00:11:58 GMT
• Title: Novel Ex-vivo Calf Brain Model with Integrated Sub-Skull Force Sensors to Access Simulated Neurosurgical Procedures
• Abstract summary: We propose and validated a portable ex-vivo force-sensing platform that measures tool-tissue interaction forces across the skull-brain interface during simulated neurosurgery.<n>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.<n>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
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Eversion-based robots can enable safe access,steering and endoscopic imaging within the spinal subarachnoid space [7.4000506819669845]
  We present a 2 mm diameter eversion-growing robotic platform that enables friction minimised extension and steering within the human spinal subarachnoid space.<n>The robot integrates a miniature endoscope for real time intrathecal visualisation and advances by pressure driven tip eversion.<n>In an intact human cadaver, the system achieved 150 mm of controlled intrathecal extension with concurrent fluoroscopic and endoscopic visualisation.
  arXiv  Detail & Related papers  (2026-07-21T16:46:00Z)
• Virtual-reality based patient-specific simulation of spine surgical procedures: A fast, highly automated and high-fidelity system for surgical education and planning [0.0]
  Surgical training involves didactic teaching, mentor-led learning, surgical skills laboratories, and direct exposure to surgery.<n>This work leverages virtual reality (VR) to provide a safe and immersive training environment.
  arXiv  Detail & Related papers  (2026-04-29T15:12:18Z)
• CT-Enabled Patient-Specific Simulation and Contact-Aware Robotic Planning for Cochlear Implantation [41.386729124620366]
  Robotic cochlear-implant insertion requires precise prediction and regulation of contact forces to minimize intracochlear trauma.<n>This paper presents a unified CT-to-simulation pipeline for contact-aware insertion planning and validation.
  arXiv  Detail & Related papers  (2026-03-05T16:13:59Z)
• Learning-based Force Sensing and Impedance Matching for Safe Haptic Feedback in Robot-assisted Laparoscopic Surgery [0.0]
  Impedance Matching Approach (NIMA) incorporates nonlinear dynamics to accurately model and render complex tool-tissue interactions in real-time.<n>NIMA achieves a mean absolute error of 0.01 (std 0.02 N), representing a 95% reduction compared to previous approaches.<n>NIMA significantly improves force fidelity, responsiveness, and precision across various surgical conditions.
  arXiv  Detail & Related papers  (2026-01-20T19:57:50Z)
• Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts [21.952265898720825]
  We propose a data-driven algorithm that preserves FEM-level accuracy while improving computational efficiency.<n>We introduce a novel human-in-the-loop mechanism into the deformation modeling process.<n>Our algorithm achieves a mean target registration error of 3.42 mm, surpassing state-of-the-art methods in volumetric accuracy.
  arXiv  Detail & Related papers  (2025-06-08T14:19:54Z)
• Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Surgical Training [33.92599418560439]
  This study proposes a novel dynamic modeling framework for coronary arteries using skeletal skinning weights.<n>It aims to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications.
  arXiv  Detail & Related papers  (2025-03-04T02:51:37Z)
• Machine learning for cerebral blood vessels' malformations [38.524104108347764]
  Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain.<n> Parameters of cerebral blood flow could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis.
  arXiv  Detail & Related papers  (2024-11-25T12:58:00Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Safe Deep RL for Intraoperative Planning of Pedicle Screw Placement [61.28459114068828]
  We propose an intraoperative planning approach for robotic spine surgery that leverages real-time observation for drill path planning based on Safe Deep Reinforcement Learning (DRL) Our approach was capable of achieving 90% bone penetration with respect to the gold standard (GS) drill planning.
  arXiv  Detail & Related papers  (2023-05-09T11:42:53Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.