Endoscopic Lumbar Discectomy (ELD) is a minimally invasive surgical procedure to remove the herniated material of a disc in the lumbar spine [1]. In the traditional approach, the surgeon holds the endoscope and inserts the instruments into its lumen to reach the surgical window and perform the discectomy. Some steps, such as opening of the ligamentum flavum, require the help of an assistant to hold the endoscope while the surgeon is occupied handling other tools: this operation results in an inevitable waste of time. To overcome this and other drawbacks of the traditional procedure, a robotic approach has been investigated, which involves the use of a collaborative robot to hold the endoscope throughout the procedure. In this context, the gripper and its actuation system must be safe, reliable and lightweight. The following paper describes a novel mechanism for manual actuation of the gripper responsible for holding the endoscope, specifically designed to allow the surgeon to have full control over the gripping force applied.

Pedicle screw placement (PSP) is a challenging procedure in spine surgery, due to poor visualization of the vertebrae and blood vessels. This has led to the development of navigation systems that improve safety and clinical outcomes. Navigation further enabled minimal invasive approaches for PSP. Current navigation systems require a preoperative computed tomography (CT) scan, followed by manual planning by the surgeon to decide screw trajectories. Intraoperative fluoroscopy imaging may be employed to register the preoperative plan with the patient's anatomy frame or even design screw trajectory planning in real-time for each pedicle individually. This is a rather lengthy procedure that involves harmful radiation to which the surgeon is exposed repeatedly. This article explores a novel approach for PSP guidance that replaces manual planning while solely relying on 3D ultrasound (US) reconstruction, hence reducing the need for radiation. Based on an intraoperative US reconstruction, a set of anatomical landmarks of the spine are identified. Subsequently, a coordinate frame is computed per vertebra. The screw path is then generated automatically using the learned relations between the landmarks and optimal screw paths. A five-fold cross-validation is conducted on the posterior surface of CT spine data involving 90 PSP paths. The algorithm is then tested on another 50 PSP paths of the 3D-printed spine phantoms corresponding to human spine CT data. Results show that for 4 mm and 6 mm diameter screws, 98% and 84% of the computed trajectories are within the required surgical precision, respectively. Using the US-based path planning led to-0.34 ± 3.66 • and-0.45 ± 4.32 • orientation errors in the sagittal and axial planes, respectively.

Spinal diseases such as spinal degeneration and scoliosis might require pedicle screw placement (PSP) as a crucial step during surgical interventions, depending on the severity. This procedure requires the drilling of a hole for placing the screw. Thanks to imaging modalities such as computed tomography and intraoperative fluoroscopy, moving from an open approach to minimally invasive surgery (MIS) has been possible and has reduced patient complications after surgery. Still, it suffers from a lack of visual feedback for the surgeon, whereas robotic-assisted spine surgery combined with such imaging modalities can improve surgical outcomes for PSP. Yet, in such MIS procedures, physical motion, such as breathing motion, can induce shifts and deformations in the spine, leading to operation errors of approximately 2-3 mm [1]. In order to correctly identify the entry point, breathing motion needs to be compensated for. External sensors, such as an optical tracking system or range imaging, can be used to measure the motion of the skin or neighboring vertebrae, close to the entry point [2], [3]. However, Saghbiny et al. showed that the amplitude of breathing motion changes over vertebrae and has a variation of 68% from the lumbar to thoracic vertebrae [4]. Therefore, this work develops an approach for estimating the motion of each entry point, enhancing the accuracy. The proposed method uses a long short-term memory network (LSTM) on top of the inner control loop to estimate breathing motion parameters for each pedicle drilling individually, and its output is utilized to update the motion model for motion compensation during robot-assisted drilling for MIS-PSP.