Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications.

Minimally invasive pedicle screw placement (MIPSP) is a widely used treatment for spine diseases. When coupled with intraoperative navigation modalities, robots may help improve surgical outcomes and reduce complications. With such a system, the application of pedicle screws has been expanded from needle insertion to the spine surgery. This paper investigates the possibility and feasibility of robot-assisted MIPSP based on ultrasound (US) guidance. The proposed system is non-radiative and fiducial-free, using purely image information to close the registration loop. Then the system automatically positions the drill tip to a planned screw trajectory and executes the drilling operation. Experiments were conducted on both ex-vivo lamb and human cadaver spines. An entry point accuracy of 2.39±1.41 mm, and orientation accuracy of 2.82±1.85∘ was found for 24 drilled trajectories on three lamb spines. On the ex-vivo human spine, the position error averaged 3.08±2.43 mm at the entry point and 4.05±2.62 mm at the stop point across 16 drilling instances. Moreover, a 87.5% success rate was reported by using Gertzbein-Robbins grade. The experimental results demonstrate the potential for offering a radiation-free alternative. Although restricted to cadaver trials, this work encourages further exploration of this technology to assist surgeons in maximizing performance in clinical practice.

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system’s accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification.

In cementless Total Hip Arthroplasty (THA), achieving high primary implant fixation is crucial for the long-term survivorship of the femoral stem. While orthopedic surgeons traditionally assess fixation based on their subjective judgement, novel vibration-analysis fixation-monitoring techniques show promising potential in providing the surgeon with objective and quantifiable fixation measurements. This study presents a dynamic response measurement protocol for implant endpoint insertion and evaluates this protocol in the presence of artificial soft tissue. After the artificial femur was prepared in accordance with the THA protocol, the implant was inserted and progressively hammered into the cavity. The Pearson Correlation Coefficient (PCC) and Frequency Response Assurance Criterion (FRAC) corresponding to each insertion hammer hit were derived from the Frequency Response Functions (FRF) corresponding to each insertion step. The protocol was repeated with the artificial femur submerged in artificial soft tissue to imitate the influence of anatomical soft tissue. The FRAC appeared overall more sensitive than the PCC. In the presence of the artificial soft tissue the technique yielded higher PCC and FRAC values earlier in the insertion process. The measurements with artificial soft tissue produced FRFs with fewer peaks, lower resonance frequencies, and overall higher damping factors. The soft tissue appears to limit the fixation-change detection capabilities of the system and a promising potential remedy to this limitation is suggested.

Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using image-guided methods that are radiation intensive and limited to the preoperative information. This work proposes a new radiation-free breach detection algorithm leveraging a non-visual sensor setup in combination with deep learning approach. Multiple vibroacoustic sensors, such as a contact microphone, a free-field microphone, a tri-axial accelerometer, a uni-axial accelerometer, and an optical tracking system were integrated into the setup. Data were collected on four cadaveric human spines, ranging from L5 to T10. An experienced spine surgeon drilled the pedicles relying on optical navigation. A new automatic labeling method based on the tracking data was introduced. Labeled data was subsequently fed to the network in mel-spectrograms, classifying the data into breach and non-breach. Different sensor types, sensor positioning, and their combinations were evaluated. The best results in breach recall for individual sensors could be achieved using contact microphones attached to the dorsal skin (85.8\%) and uni-axial accelerometers clamped to the spinous process of the drilled vertebra (81.0\%). The best-performing data fusion model combined the latter two sensors with a breach recall of 98\%. The proposed method shows the great potential of non-visual sensor fusion for avoiding screw misplacement and accidental bone breaches during pedicle drilling and could be extended to further surgical applications.

Composite bone replicas are widely used in biomechanical testing as alternatives to cadaveric specimens, with numerical models often complementing or replacing experiments. The reliability of these models depends strongly on accurate material parameters. This study investigates a fourth-generation Sawbones composite L5 vertebra, updating cortical material properties under isotropic and transversely isotropic modelling assumptions. Finite element models were calibrated using free-free experimental modal analysis, revealing differences between manufacturer-provided material properties and the measured specimen behaviour. For both models, matching the specimen mass required reducing the cortical density from 1.64 g/cm³ to 1.423 g/cm³. In the isotropic model, the Young’s modulus was reduced from 16,000 MPa to 6500 MPa. In the transversely isotropic model, longitudinal and transverse Young’s moduli were reduced from 16,000 MPa and 11,000 MPa to 6400 MPa and 5500 MPa, respectively, while the shear moduli decreased from 4370 MPa and 6350 MPa to 3500 MPa and 2540 MPa. In both models, the Poisson’s ratio was increased from 0.26 to 0.30. These updates reduced the average eigenfrequency error to 6.12% (isotropic) and 5.83% (transversely isotropic), with the first five modes errors reduced to 3.10% and 2.80%, respectively, substantially improving numerical representation of L5 vertebral mechanics. The updated vertebral FE model and accompanying workflow enhance the reliability of future FE analyses, improve interpretation of Sawbones vertebra biomechanical results, and support vibration-based biomechanical applications such as implant fixation assessment.