To close the surgeon in the control loop is able to take advantages of the fertile sensing information and intelligence from human being so that the complex and unpredictable surgical procedure can be better handled properly. However, there is also weakness to be strengthened. For example, the motion control of the human being is not as accurate as required. Assisted equipment for such purpose may be helpful to the procedure. Exerting large forces during the cutting process may exhaust the operator and cause fatigue. The operator may need power assistance during the surgery procedure. The response speed of human being's may not be adequate to take immediate action in certain critical situations. The constraints from the limbs and human's attention usually cause a significant delay to react the critical situations. This may lead to serious damage by failing to response immediately. For example, in knee replacement procedure, the shape of the knee joint has to be prepared by performing bone resection procedure. The surgeon cuts the bone at certain position and orientation with the help of the cutting jig from the surgical planning. The cutter cut the bone by inserting vibration saw through the slot of the cutting block. The surgeon has to stop the cutter right after the bone has been cut through by the saw so that no surrounding soft tissue, blood vessel, and even important nerves, be damaged. Currently the tactile sensing from the hand is heavily relied on by the surgeon. He has also to be experienced and dexterous. If he fails to draw back the cutter after the bone being cut through, damages to the patient may occur. Therefore there is need to develop a cutting tool, which is intelligent, knows where it is, and is able to judge what the cutting situation is, and further assists the operator to stop the cutter in case that the operator fails to do so, or too slow to response. The benefit to the patient as well as the surgeon will be significant. Therefore the purpose of the paper is to develop an algorithm to implement such functionality of auto-detection of bone cutting through for a bone cutting tool when the cutter has cut through the bone. By the developed method, the intelligent cutter can effectively detect the cutting through condition and then adopt an immediate reaction to prevent the cutter from going further and to avoid unexpected damage.

An auto-detection scheme has also been developed for assisting the operator in judging whether the cutter has reached the boundary or not. The auto-detection scheme is based on analyzing the cutting force pattern in conjunction with a bilateral force controller for the hand's on robot. The bilateral force controller consists of two force controllers. The force controller on the master calculates the feedrate of the cutter according to the push force by the operator. Meanwhile, the operator can also feel the cutting force by the force controller on the slave site, which revises the feedrate of the cutter by the measured cutting force. The operator can kinesthetically feel the cutting force from the variation of the feedrate. When the cutter breaks through the boundary of the bone, the cutting force will drop suddenly to almost zero. When this special force pattern occurs, an acknowledge signal of bone been broken through will be activated. The subsequent action will be followed to stop the cutter going further. We implement this concept by defining a “cutting admittance” indicating the resistance encountered by the cutting during bone resection at different cutting feedrate. During the bone resection, the cutting admittance varies from cutting through hard or soft portions of bone. As reaching the cutting boundary, the cutting admittance will suddenly increase. A threshold value will experimentally be determined to indicate cutting through condition. Together with pushing force by the human operator, the criterion for cutting-through detection is defined. Once cutting through is detected, the admittance for cutter movement will be set zero. The cutter stops going further and the operator feel like hitting a virtual wall in front.

In this paper we proposed a human robot cooperative operation method by which the robotic system can intelligently detect where the cutter has cut through the bone. Characterisation of bone cutting procedure was performed. This auto detection scheme was developed by analyzing the information of the motion and cutting force information during the bone cutting process, no medical images are required. The auto-detection bone cut through was able to transfer the experiences of human being to quantitative modeling. The developed model has been tested for its applicability and robustness by saw bones and pig's knee joints. Results have shown the virtual wall generated by the real-time bone detection scheme and active constraint control is very accurate and capable of providing a safety enhance module for computer assisted orthopaedic surgery, in particular, in total knee replacement. By this method the robot system can accomplish a safe and accurate bone cutting with a complement of an imageless navigation system and results in a low cost, but safe and effective surgical robot system.

To introduce a new robot-assisted surgical system for spinal posterior fixation which called TiRobot, based on intraoperative three-dimensional images. TiRobot has three components: the planning and navigation system, optical tracking system and robotic arm system. By combining navigation and robot techniques, TiRobot can guide the screw trajectories for orthopedic surgeries. In this randomised controlled study approved by the Ethics Committee, 40 patients were involved and all has been fully informed and sign the informed consent. 17 patients were treated by free-hand fluoroscopy-guided surgery, and 23 patients were treated by robot-assisted spinal surgery. A total of 190 pedicle screws were implanted. The overall operation times were not different for both groups. None of the screws necessitated re-surgery for revised placement. In the robot-assisted group, assessment of pedicle screw accuracy showed that 102 of 102 screws (100%) were safely placed (<2 mm, category A+B). And mean deviation in entry point was 1.70 +/− 0.83mm, mean deviation in end point was 1.84 +/− 1.04mm. In the conventional freehand group, assessment of pedicle screw accuracy showed that 87 of 88 (98.9%) were safely placed (<2 mm, category A+B), 1 screw fall in category C, mean deviation in entry point was 3.73 +/− 2.28mm, mean deviation in end point was 4.11 +/− 2.31mm. This randomised controlled study verified that robot-assisted pedicle screw placement with real-time navigation is a more accuracy and safer method, and also revealed great clinical potential of robot-assisted surgery in the future

While image guidance and neuro-navigation have enabled a more accurate positioning of pedicle implants, robot-assisted placement of pedicle screws appears to overcome the disadvantages of the two first systems. However, recent data concerning the superiority of robots currently available to assist spinal surgeons in the accurate positioning of implants are conflicting. The aim of our study was to evaluate the percentage of accurate positioning of pedicle screws inserted using a new robotic-guidance system.

Patients were operated on successively by the same surgeon using robotic-assistance (RA; n=40) or by the freehand conventional technique (FH; n=54). Ten and eleven patients from the robot (RG) and freehand (FHG) groups respectively, age-matched and all suffering from degenerative lumbar spine disease were compared. Patient characteristics as well as the duration of the operation and of exposure to X-rays were recorded. The Gertzbein Robbins classification was used to evaluate implant placement. Data wer compared between the groups. Pedicle screw placement in RG patients was achieved using the ROSA™ (Medtech) robot comprising a compact robotic arm on a floor-fixable mobile base. By permanently monitoring the patient's movements, this image-guided tool helps more accurately to pinpoint the pedicle entry point and to control the trajectory.

The mean age of patients in each group (RG and FHG) was 63 years. Mean BMI and operating time among the RG and FHG were respectively 26 and 27 kg/m², and 187 and 119 min. Accurate placement of the implant (score A-B) was achieved in 97.2% of patients in the RG (n=36) and in 92.6% of those in the FHG (n=54). Four implants in the RG were placed manually following failed robotic assistance. The mean duration of X-ray exposure per patient was 1 min 42s in the RG and 41s in the FHG.

We report a higher rate of accuracy with robotic assistance as compared to the FH technique. Exposure time was greater in the RG partly due to the fluoroscopic control of the implants required for this pilot study of feasibility. Limitations of the study include its small sized and non-randomised sample. Nevertheless, these preliminary results are encouraging for the development of new robotic techniques for spinal surgery.

A functional total knee replacement has to be well aligned, which implies that it should lie along the mechanical axis and in the correct axial and rotational planes. Incorrect alignment will lead to abnormal wear, early mechanical loosening, and patellofemoral problems. There has been increased interest of late in total knee arthroplasty with robot assistance. This study was conducted to determine if robot-assisted total knee arthroplasty is superior to the conventional surgical method with regard to the precision of implant positioning. Twenty knee replacements of ten robot-assisted and another ten conventional operations were performed on ten cadavers. Two experienced surgeons performed the surgery. Both procedures were undertaken by one surgeon on each cadaver. The choice of which was to be done first was randomized. After the implantation of the prosthesis, the mechanical-axis deviation, femoral coronal angle, tibial coronal angle, femoral sagittal angle, tibial sagittal angle, and femoral rotational alignment were measured via three-dimensional CT scanning. These variants were then compared with the preoperative planned values. In the robot-assisted surgery, the mechanical-axis deviation ranged from −1.94 to 2.13° (mean: −0.21°), the femoral coronal angle ranged from 88.08 to 90.99° (mean: 89.81°), the tibial coronal angle ranged from 89.01 to 92.36° (mean: 90.42°), the tibial sagittal angle ranged from 81.72 to 86.24° (mean: 83.20°), and the femoral rotational alignment ranged from 0.02 to 1.15° (mean: 0.52°) in relation to the transepicondylar axis. In the conventional surgery, the mechanical-axis deviation ranged from −3.19 to 3.84°(mean: −0.48°), the femoral coronal angle ranged from 88.36 to 92.29° (mean: 90.50°), the tibial coronal angle ranged from 88.15 to 91.51° (mean: 89.83°), the tibial sagittal angle ranged from 80.06 to 87.34° (mean: 84.50°), and the femoral rotational alignment ranged from 0.32 to 4.13° (mean: 2.76°) in relation to the transepicondylar axis. In the conventional surgery, there were two cases of outlier outside the range of 3° varus or valgus of the mechanical-axis deviation. The robot-assisted surgery showed significantly superior femoral-rotational-alignment results compared with the conventional surgery (p=0.006). There was no statistically significant difference between robot-assisted and conventional total knee arthroplasty in the other variants. All the variants were measured with high intraobserver and interobserver reliability. In conclusion, Robot-assisted total knee arthroplasty showed excellent precision in the sagittal and coronal planes of the three-dimensional CT. Especially, better accuracy in femoral rotational alignment was shown in the robot-assisted surgery than in the conventional surgery despite the fact that the surgeons who performed the operation were more experienced and familiar with the conventional surgery than with robot-assisted surgery. It can thus be concluded that robot-assisted total knee arthroplasty is superior to the conventional total knee arthroplasty

Introduction. Mechanically aligned total knee arthroplasty(TKA) relies on restoring the hip-knee-ankle angle of the limb to neutral or as close to a straight line as possible. This principle is based on studies that suggest limb and knee alignment is related long term survival and wear. For that cause, there has been recent attention concerning computer-assisted TKA and robot is also one of the most helpful instruments for restoring neutral alignment as known. But many reported data have shown that 20% to 25% of patients with mechanically aligned TKA are dissatisfied. Accordingly, kinematically aligned TKA was implemented as an alternative alignment strategy with the goal of reducing prevalence of unexplained pain, stiffness, and instability and improving the rate of recovery, kinematics, and contact forces. So, we want to report our extremely early experience of robot-assisted TKA planned by kinematic method. Materials and Methods. This study evaluated the very short term results (6 weeks follow up) after robot-assisted TKA aligned kinematically. 50 knees in 36 patients, who could be followed up more than 6 weeks after surgery from December 2014 to January 2015, were evaluated prospectively. The diagnosis was primary osteoarthritis in all cases. The operation was performed with ROBODOC (ISS Inc., CA, USA) along with the ORTHODOC (ISS Inc., CA, USA) planning computer. The cutting plan was made by single radius femoral component concept, each femoral condyles shape-matched method along the transverse axis using multi-channel CT and MRI to place the implant along the patient's premorbid joint line. Radiographic measurements were made from long bone scanograms. Clinical outcomes and motion were measured preoperatively and 6 weeks postoperatively. Results. The range of motion increased from preoperative mean 113.4 (±5.4, 85 to 130) to postoperative mean 127.3 (±7.4, 90 to 140) at last follow up. The mean knee score and functional score improved from 35.4 (±10.3, 10 to 55) and 30.1 (±7.7, 10 to 60) before surgery to 88.6 (±5.8, 60 to 100) and 90.7 (±9.6, 60 to 100) at last follow up. The WOMAC score was improved from 52(±15.5) to 20(±14.8) at last follow up. The postoperative Hip-knee-ankle alignment was −1.3±2.8. The femoral component was 2.1 valgus and tibial component was 2.8 varus along the mechanical axis in coronal plane. There were no complications and failures. Conclusion. On the basis of our results, we are cautiously optimistic about robot-assisted TKA by kinematically alignment. More anatomic alignment of the implant can be associated with better flexion and better clinical outcomes scores in the kinematically aligned method in our thinking. But, at this starting point, more comparative studies with mechanical aligned group are needed and we must explore about implant survivalship issues and implant loading issues in dynamic and static condition that someone is worrying about. If the problem can be solved, there is no use worrying about it in our thinking. And what is more, the robot-assisted surgery will be very useful especially in those cases of severely deformed knees and distorted anatomy to be aligned kinematically

INTRODUCTION. Allograft reconstruction after resection of primary bone sarcomas has a non-union rate of approximately 20%. Achieving a wide surface area of contact between host and allograft bone is one of the most important factors to help reduce the non-union rate. We developed a novel technique of haptic robot-assisted surgery to reconstruct bone defects left after primary bone sarcoma resection with structural allograft. METHODS. Using a sawbone distal femur joint-sparing hemimetaphyseal resection/reconstruction model, an identical bone defect was created in six sawbone distal femur specimens. A tumor-fellowship trained orthopedic surgeon reconstructed the defect using a simulated sawbone allograft femur. First, a standard, ‘all-manual’ technique was used to cut and prepare the allograft to best fit the defect. Then, using an identical sawbone copy of the allograft, the novel haptic-robot technique was used to prepare the allograft to best fit the defect. All specimens were scanned via CT. Using a separately validated technique, the surface area of contact between host and allograft was measured for both (1) the all-manual reconstruction and (2) the robot-assisted reconstruction. All contact surface areas were normalized by dividing absolute contact area by the available surface area on the exposed cut surface of host bone. RESULTS. The mean area of contact between host and allograft bone was 24% (of the available host surface area) for the all-manual group and 76% for the haptic robot-assisted group (p=0.004). CONCLUSIONS. This is the first report to our knowledge of using haptic robot technology to assist in structural bone allograft reconstruction of defects left after primary bone tumor resection. The findings strongly indicate that this technology has the potential to be of substantial clinical benefit. Further studies are warranted

INTRODUCTION. Unicompartmental knee arthroplasty (UKA) allows replacement of a single compartment in patients who have isolated osteoarthritis as a minimally invasive procedure. However, limited visualization of the surgical site provides challenges in ensuring accurate alignment and placement of the prosthesis. With robot-assisted surgery, correct implant positioning and ligament balancing are obtainable with increased accuracy. To date, there has not been a large series reported in the literature of UKAs performed with robotic assistance. The aim of this study was to examine the clinical outcomes of robot-assisted UKA patients. METHODS. 510 patients who underwent robotic-assisted UKA between July 2008 and June 2010 were identified (average age 63.7 years, range: 22 to 28 years). Clinical outcomes were evaluated using the Oxford Knee Score (OKS) and patients without recent follow-up were phoned. Revision rate and time to revision were also examined. RESULTS. Average length of stay was 1.4 days (range: 1 to 7 days). There was minimal blood loss with most procedures. There were two intra-operative complications, both in early patients in the series. The first intra-operative complication was broken alignment pins in both the femur and tibia. In the second complication, preparation was finished manually with a burr due to registration problems with the software. Both patients were doing well at most recent follow up and neither experienced further complications. At latest clinical follow-up, patients reported a mean OKS of 36.1 + 9.92. The revision rate was 2.5% with 13 patients either converted from an inlay to onlay prosthesis or conversion to TKA. The most common indication for revision was tibial component loosening, followed by progression of arthritis. One patient was revised due to infection. Mean time to revision was 9.55 + 5.48 months (range: 1 to 19 months). CONCLUSION. UKA with a robotic system provides good pain relief and functional outcomes at short-term follow-up. Ensuring correct component alignment and ligament balancing increases the probability of a favorable outcome. Proper patient selection for appropriate UKA candidates remains an important factor for successful outcomes. In combination with robotic assistance there can be a reduction in many of the failures seen with early systems

In robot-assisted orthopaedic surgery, registration is a key step, which defines the position of the patient in the robot frame so that the preoperative plan can be performed. Current registration methods have their limitations, such as the requirement of immobilisation of the limbs or the line of sight (LOS) issues. These issues cause inconvenience for the surgeons and interrupt the surgical workflow in the operating room. Targetting these issues of current registration methods, we propose a camera-robot registration system for joint replacement. The bone geometry, which is measured directly by a depth camera, is aligned to a preoperatively obtained bone model to calculate the pose of the target. Simultaneously, in order to avoid registration failure caused by LOS interruptions, the depth camera tracks objects that may occlude the target bone, and a robot manipulator is used to move the camera away from the nearest obstacle. The optimal camera motion is calculated based on the position and velocity of the obstacle, which avoids the occlusion efficiently without changing the target position in the camera frame. Inverse kinematics of the robot is used to project the Cartesian velocity of the end-effector into the joint space, with kinematic singularities considered for stable robotic control. An admittance controller is designed as the human-robot interface so that the surgeon can directly set the robot configuration by hand according to the actual environment. Simulations and experiments were conducted to test the performance. The results show that the proposed obstacle avoidance method can effectively increase the distance between the obstacle and the LOS, which lowers the risk of registration failure due to obstacle occlusion. This pilot study is promising in reducing distractions to the surgeon and can help achieve a fluent and surgeon-centred workflow

One of the more difficult tasks in surgery is to apply the optimal instrument forces and torques necessary to conduct an operation without damaging the tissue of the patient. This is especially problematic in surgical robotics, where force-feedback is totally eliminated. Thus, force sensing instruments emerge as a critical need for improving safety and surgical outcome. We propose a new measurement system that can be used in real fracture surgeries to generate quantitative knowledge of forces/torques applied by surgeon on tissues. We instrumented a periosteal elevator with a 6-DOF load-cell in order to measure forces/torques applied by the surgeons on live tissues during fracture surgeries. Acquisition software was developed in LabView to acquire force/torque data together with synchronised visual information (USB camera) of the tip interacting with the tissue, and surgeon voice recording (microphone) describing the actual procedure. Measurement system and surgical protocol were designed according to patient safety and sterilisation standards. The developed technology was tested in a pilot study during real orthopaedic surgery (consisting of removing a metal plate from the femur shaft of a patient) resulting reliable and usable. As demonstrated by subsequent data analysis, coupling force/torque data with video and audio information produced quantitative knowledge of forces/torques applied by the surgeon during the surgery. The outlined approach will be used to perform intensive force measurements during orthopaedic surgeries. The generated quantitative knowledge will be used to design a force controller and optimised actuators for a robot-assisted fracture surgery system under development at the Bristol Robotics Laboratory

The use of robots in orthopaedic surgery is an emerging field that is gaining momentum. It has the potential for significant improvements in surgical planning, accuracy of component implantation and patient safety. Advocates of robot-assisted systems describe better patient outcomes through improved pre-operative planning and enhanced execution of surgery. However, costs, limited availability, a lack of evidence regarding the efficiency and safety of such systems and an absence of long-term high-impact studies have restricted the widespread implementation of these systems. We have reviewed the literature on the efficacy, safety and current understanding of the use of robotics in orthopaedics.

Cite this article: Bone Joint J 2015; 97-B:292–9.