Emmanuel Vander Poorten

Responsibility and control in the upcoming and increasingly autonomous orthopedic and neurosurgical interventions.The paper by Davoodi et al. demonstrates the potential of robot-assisted surgery for pedicle screw placement (PSP) in the lumbar spine, focusing on an optical navigation and registration approach as an alternative to approaches that rely on intra-operative fluoroscopy and CT registration. Validated on both synthetic models and ex-vivo bovine spines, average position errors of respectively 2.61mm and 1.51mm, and orientation errors of respectively 2.65° and 2.30° result. This demonstrates the potential of their approach to reduce radiation exposure of patients and clinicians. The authors conclude that although there are still challenges to address, robots equipped with optical tracking show considerable potential for clinical use.While Davoodi et al. focus on lumbar spine fixation, Lin et al. extend robotic screw placement to cervical spine procedures. Lin et al. reported achieving 97.2% clinically acceptable robot-assisted placements of anterior transpedicular screws (ATPS) in the subaxial cervical spine using five human specimens. The study made use of the uOssa-Nav-s750 system (Wuhan United Imaging Surgical Co., Ltd., Wuhan, China), which is a robotic framework that employs 3D C-arm imaging, point-based rigid registration, and semi-automatic trajectory alignment. Once the robot is automatically aligned to the preplanned screw trajectory the surgeon performs the final fine adjustment and locking before drilling. The study on ex-vivo human specimens suggests that a robotic approach is feasible even for challenging interventions such as cervical ATPS, where current procedures are conducted manually. Despite demonstrated biomechanical advantages, ATPS is considered technically highly demanding today. Lin's study showed that, provided further validation is conducted, robotic approaches may very well help broaden the adoption of ATPS in clinical settings in the future.Inigo et al. focus on vertebroplasty, a minimally invasive procedure used to stabilize weakened or fractured vertebral bodies. In contrast to PSP where the goal is to accurately drill trajectories in which subsequently screws are placed, vertebroplasty requires direct needle insertion through the pedicle into the vertebral body in one go. Via the hollow channel of the needle, bone cement can then be injected to strengthen the fractured vertebral body. Again, in contrast to PSP, vertebroplasty typically cannot make use of pre-operative CT. To counter this lack of information, Inigo et al.'s Spine-DART system combines atlas priors with deep learning, differential rendering and a multi-view learned similarity loss ultimately requiring only two arbitrary X-ray images for 3D vertebral reconstruction and planning 3D transpedicular paths. This renders Spine-DART highly practical in real intraoperative settings. Trained on the colon and NMDID data-set, the method was tested on the VerSe-small and a single cadaver experiment, achieving competitive reconstruction results and plans that are considered clinically viable in the majority of cases.Finally, in Gaytan et al. the authors describe the ability of the Robossis V2 system to support femur fraction reduction, by assisting in the re-alignment of the different femur bone fragments after fracture to restore anatomical position and promote healing. The paper describes the mechanical design and focusses on the improved workspace of Robossis V2 compared to its V1 predecessor, showing workspace coverages well beyond the clinically required alignment ranges. Further work on intuitive control and workflow integration is planned to optimize surgeon-robot collaboration in this high-force safety-critical application.As illustrated across the works showcased in this Topic, advancing the capabilities of surgical robotics demands interdisciplinary research across multiple fronts. Aside from progress in actuation, sensing, and control, a key challenge is to improve the real-time understanding of the surgical scene to deal with deformation, physiological phenomena and uncertainty. The Research Topic showed that there is a major recent focus on developing new clinical workflows that exploit diverse real-time sensors and real-time data-processing to enable real-time, AI-empowered decision-making in complex surgical environments. As surgical robot capabilities improve and as robotic systems gain autonomy, ensuring their reliability through novel control and planning strategies is crucial for handling complex situations without compromising patient safety. Additionally, seamless data integration from visual and non-visual sources is essential for accurate decision-making. In parallel with increased autonomy, improvements in human-machine collaboration are needed to ensure robustness, safety and efficiency of operation. Beyond more intuitive interfaces that enhance human oversight, improved communication of intention among autonomous agents could strengthen human assurance. This, in turn, may facilitate smoother downstream transitions and support the subsequent regulatory processes and hence are critical to bring orthopedics and neurosurgical interventions to a new level of sophistication.

Orthopaedic spine surgeries demand millimeter-level precision to avoid complications arising from tool manipulation. While ultrasound (US)-guided robotic systems have emerged in research as radiation-free alternatives, clinical systems still rely on fluoroscopy, which exposes patients and surgical teams to ionising radiation. Existing robotics systems are typically single-robot systems used for guidance in combination with fluoroscopy or CT navigation. Dual-robot systems—dedicating separate robots to imaging and tool manipulation—could enhance efficiency but require accurate and robust calibration. Existing calibration methods, referred to as the Step-by-Step Calibration approach, primarily rely on external optical trackers or fiducial markers to sequentially estimate individual transformations. These methods are prone to cumulative error propagation and are unsuitable for intraoperative use due to line-of-sight constraints. In this work, we propose a dual-robot spine surgery system featuring a drilling arm and a US arm for scanning the anatomy. A novel markerless calibration pipeline for the dual-robot system is proposed that uses a US probe as an imaging camera to simultaneously estimate the full closed-chain unknown transformation matrices. Our method eliminates the need for external sensors or manual drill tip pose identification by automatically extracting it from 3D US reconstructions. A Lie algebra-based optimisation algorithm ensures global consistency and orthogonality of rotation matrices while estimating the unknown values. Additionally, a fast local recalibration module enables rapid intraoperative tool updates, minimally interrupting the workflow. Experimental validation demonstrates that the proposed framework achieves a median translational error of 0.71mm and an angular error of 1.14 degree in in-vitro trials, confirming its clinical potential. This approach advances radiation-free orthopaedic navigation by providing a practical, accurate, and fast calibration solution compatible with surgical constraints.

Needle-based interventions are routine in clinical practice, yet clinicians lack reliable, precise, real-time estimates of the tip-to-target distance—–a key determinant of safety and efficiency. To bridge this critical clinical gap, physics-based models that maps electrical bio-impedance (EBI) measurements to the absolute tip-to-target distance by leveraging tissue-intrinsic electrical properties encoded in EBI are presented in this work. A tripolar electrode configuration is adopted to ensure tool-independence and broad compatibility across instruments and anatomical sites. The optimal layout of electrode radius, inter-electrode spacing, and placement was identified through a systematic parametric study. The model was validated across finite-element simulations, saline solution experiments, and ex vivo layered tissue. In all settings, proximity distance estimates were accurate and robust across different insertion angles. In simulation, it achieved 0.55 mm RMSE/1.06 mm MAE when the target layer was less conductive than the first layer and 0.40 mm RMSE/1.21 mm MAE when it was more conductive. In saline, four insertion angles yielded millimeter errors with no angle dependence. In ex vivo layered tissue, accuracy remained near the millimeter level across angles (RMSE 1.00–1.13 mm and MAE 1.30–1.58 mm), which is slightly lower than saline yet consistent across orientations. Together, these results establish a general, instrument-agnostic framework for absolute proximity distance sensing during needle interventions with the potential to enhance safety and efficiency in minimally invasive procedures. Furthermore, by first focusing on a two-layer model, this work provides a clear pathway to multi-layer tissue modeling and broader anatomical generalization.

Open spina bifida (OSB), a congenital anomaly caused by incomplete spinal column closure, leads to progressive nerve damage. While multi-port minimally invasive procedures are being optimized clinically, single-port access (SPA) surgery presents a promising, less invasive alternative. Beyond reducing scarring, SPA may also decrease the risk of iatrogenic preterm premature rupture of membranes and enhance maternal recovery, as suggested by outcomes in other minimally invasive interventions. However, technical challenges persist in SPA implementations, particularly in instrument miniaturization and control precision. To facilitate this approach, a macro-micro multi-arm robotic system is under development. The system features an 11-mm-diameter insertion port to deploy four instrument arms. The arm design incorporates a hybrid backbone structure. A macro proximal section of concentric tubes provides stable deployment, while a micro distal flexible segment enables the dexterous manipulation. This paper presents dedicated modeling and control strategies to address actuator and transmission nonlinearities arising from stringent miniaturization requirements. A hybrid framework combining joint-level feedback with deep-learning-based feedforward compensation mitigates limitations imposed by pneumatic artificial muscles and tendon transmissions. The system is validated through trajectory-following experiments, each conducted over 10 trials. Preliminary results show an average root-mean-squared error of 0.28 mm, an average maximum absolute error of 0.85 mm, and an average standard deviation of 0.18 mm across both trajectories. These results demonstrate consistent sub-millimeter precision, satisfying the requirement for OSB repair. The feasibility of the system was then verified through a phantom study.

In current clinical practice, fluoroscopy is used to track the catheter shape during endovascular navigation. To reduce radiation exposure for clinicians and patients, alternative approaches relying on electromagnetic (EM) tracking and fiber-optic shape sensing (FOSS) have been investigated. EM sensors can be fragile and sensitive to EM disturbances. FOSS alone does not provide localization which is essential for catheter navigation. In this work, a method for localizing a catheter’s shape-sensing fiber with respect to a static reference frame without relying on additional sensors is proposed. The method exploits the approximate follow-the-leader motion that typically arises during catheter insertion. The proposed approach foresees hereto a method to dynamically identify stable reference segments to be used for localization. This method explores the potential to offer both shape sensing and localization with a single shape sensing fiber. The proposed method was validated in 3D vessel mock-ups. Results demonstrate the feasibility of the approach, with tip localization errors of −0.29 $\pm$ 1.86 mm and 3.82 $\pm$ 2.66 mm along and perpendicular to the vessel centerline, respectively, achieved over insertion depths up to 800 mm. This work offers an important step toward reducing reliance on radiation-based imaging and EM sensors in catheter-based interventions.

Reliable microneedle guidance during subretinal injection remains a major clinical challenge. One of the reasons is that surgeons must infer the 3D position of the needle from a stereomicroscope that offers limited depth perception and from narrow B-scan iOCT cross-sections that cover only a fraction of the operative field. While spiral OCT scanning has been previously introducedfor fast and continuous volumetric visualization of the surgical field, our work builds upon this technique by focusing on the development of a lightweight, geometry-based pose extraction framework. The proposed pipeline estimates the pose of the needle directly from the sparse 3-D point cloud generated by a single spiral OCT sweep containing just 2000 A-scans ($\approx$ 20 ms acquisition at 100 kHz). This reduction in A-scans shortens OCT acquisition time for needle tracking and, together with the elimination of pixel-wise segmentation and neural-network inference, slashes computational load and facilitates real-time deployment. A multi-pass RANSAC line fit exploits prior knowledge of needle geometry and trocar-constrained approach vectors to converge rapidly and reject outliers. Validation on ex vivo porcine eyes demonstrates a median orientation error below 3.0$^{\circ }$ and a lateral tip error under 20 $\mathrm{\mu }$m, with an end-to-end latency of 46ms that supports pose updates beyond 20Hz. A dense, continuously updated 3-D rendering is also generated to let surgeons see both instrument trajectory and local retinal topography at a glance.