Computer-assisted Orthopaedic Surgery Research Articles

Application-Specific Learning Curve With a Modern Computer-Assisted Orthopedic Surgery System for Joint Arthroplasty

Abstract A commonly acknowledged barrier for the adoption of new computer-assisted orthopedic surgery (CAOS) technologies relates to a perceived long and steep learning curve. However, this perception has not been objectively tested with the consideration of surgeon-specific learning approaches. This study employed the cumulative sum control chart (CUSUM) to investigate individual surgeon's learning of CAOS technology by monitoring the stability of the surgical process regarding surgical time. Two applications for total knee arthroplasty (TKA) and two applications for total shoulder arthroplasty (TSA) provided by a modern CAOS system were assessed with a total of 21 surgeons with different levels of previous CAOS experience. The surgeon-specific learning durations identified by CUSUM method revealed that CAOS applications with “full guidance” (i.e., those that offer comprehensive guidance, full customization, and utilize CAOS-specific instrumentation) required on average less than ten cases to learn, while the streamlined application designed as a CAOS augmentation of existing mechanical instrumentation demonstrated a minimal learning curve (less than three cases). During the learning phase, the increase in surgical time was found to be moderate (approximately 15 min or less) for the “full guidance” applications, while the streamlined CAOS application only saw a clinically negligible time increase (under 5 min). The CUSUM method provided an objective and consistent measurement on learning, and demonstrated, contrary to common perception, a minimal to modest learning curve required by the modern CAOS system studied.

Patient-Specific Implants for Focal Cartilage Lesions in The Knee: Implant Survivorship Analysis up to Seven Years Post-Implantation

In the quest for increased surgical precision and improved joint kinematics, Computer-Assisted Orthopedic Surgery (CAOS) shows promising results for both total and partial joint replacement. In the knee, computer-assisted joint design can now be applied to the treatment of younger patients suffering pain and restriction of activity due to focal defects in their femoral articular cartilage. By taking MRI scans of the affected knee and digitally segmenting these scans, we can identify and map focal defects in cartilage and bone. Metallic implants matched to the defect can be fabricated, and guide instrumentation to ensure proper implant alignment and depth of recession in the surrounding cartilage can be designed from segmented MRI scans. Beginning in 2012, a series of 682 patient-specific implants were designed based on MRI analysis of femoral cartilage focal defects, and implanted in 612 knees. A Kaplan-Meier analysis found a cumulative survivorship of 96% at 7-year follow-up from the first implantation. Fourteen (2.3%) of these implants required revision due to disease progression, incorrect implant positioning, and inadequate lesion coverage at the time of surgery. These survivorship data compare favorably with all other modes of treatment for femoral focal cartilage lesions and support the use of patient-specific implants designed from segmented MRI scans in these cases.

Computer-Assisted Orthopedic and Trauma Surgery.

There are many ways in which computer-assisted orthopedic and trauma surgery (CAOS) procedures can help surgeons to plan and execute an intervention. This study is based on data derived from a selective search of the literature in the PubMed database, supported by a Google Scholar search. For most applications the evidence is weak. In no sector did the use of computer-assisted surgery yield any relevant clinical or functional improvement. In trauma surgery, 3D-navigated sacroiliac screw fixation has become clinically established for the treatment of pelvic fractures. One randomized controlled trial showed a reduction in the rate of screw misplacement: 0% with 3D navigation versus 20.4% with the conventional procedure und 16.6% with 2D navigation. Moreover, navigation-assisted pedicle screw stabilization lowers the misplacement rate. In joint replacements, the long-term results showed no difference in respect of clinical/functional scores, the time for which the implant remained in place, or aseptic loosening. Computer-assisted procedures can improve the precision of certain surgical interventions. Particularly in joint replacement and spinal surgery, the research is moving away from navigation in the direction of robotic procedures. Future studies should place greater emphasis on clinical and functional results.

Toward Automated Tissue Classification for Markerless Orthopaedic Robotic Assistance

A markerless computer aided orthopaedic platform will require a complex computer vision system to isolate and track rigid bodies used to localize a robot to a patient. Isolating rigid bodies such as bone requires accurate segmentation and this study explores using diffuse laser reflectivity to accurately classify tissue. Lasers (red at 650nm and infrared – IR – at 850nm) intersected four material types; cartilage, ligament, muscle and metal surgical tools within a controlled cadaveric setup. Images were captured with an infrared CMOS sensor, pre-processed to isolate laser centers, and resized to test information requirements. Images for both laser types were scaled from $5\times5$ pixels to $30\times30$ pixels and trained on a convolutional neural network, GoogLeNet. At sizes above $15\times15$ pixels, the IR laser had a higher classification accuracy, reaching 97.8% at $30\times30$ pixels, whereas the red laser peaked at 94.1%. It was shown as not possible to qualitatively identify materials that were not trained in the network based on their probability outputs. Further work will be performed to classify multiple points in a single scene as a step toward segmenting entire surgical views for markerless Computer Assisted Orthopedic Surgery (CAOS) systems.

Use of Navigation-Enhanced Instrumentation to Mitigate Surgical Outliers During Total Knee Arthroplasty.

Computer-assisted orthopedic surgery improves mechanical alignment and the accuracy of surgical cuts in the context of total knee arthroplasty (TKA). A simplified, navigation-enhanced instrumentation system was assessed to determine whether the same effects could be achieved with a less intrusive system. Two cohorts of surgeons (experienced and trainees) performed a series of TKA cuts using models with and without navigation-enhanced instrumentation. The accuracy of each system was determined via the rate of outliers, measured as any cut that deviated from the planned cut by more than 2° or 2 mm. The effect of experience level was limited, with only the outlier rate for tibial varus or valgus measurement showing a significant difference between user groups with conventional instrumentation (P=.004). The use of navigation-enhanced instrumentation significantly reduced the total outlier rate compared with conventional instrumentation from 35% to 4% for experienced users (P<.001) and from 34% to 10% for trainees (P<.001). These results suggest that navigation-enhanced instrumentation is a viable alternative to conventional instrumentation to reduce outlier rates and improve cut accuracy. This trial also showed that additional experience may not correlate with improved surgical accuracy. Outliers may not reflect individual surgical ability as much as limitations of the instrumentation or other unidentified factors. [Orthopedics. 2021;44(1):54-57.].

Generalized 3-D Point Set Registration With Hybrid Mixture Models for Computer-Assisted Orthopedic Surgery: From Isotropic to Anisotropic Positional Error

Registering two point sets (PSs) is an essential problem in medical robotics and computer-assisted surgery (CAS). As one typical example, in computer-assisted orthopedic surgery (CAOS), the preoperative scan has to be aligned with the intraoperative scan accurately. In this article, we first formally formulate the generalized PS registration problem in a probabilistic manner. Especially, not only positional but also orientational information is incorporated into the registration. Notably, the positional error is assumed to obey a multivariate Gaussian distribution to accommodate the anisotropic noise. The expectation–maximization (EM) framework is utilized to solve the maximum likelihood (ML) problem. In the E-step, the correspondence probabilities between points in two generalized PSs are computed. In the M-step, the constrained optimization problem with respect to the rigid transformation matrix is reformulated as an unconstrained one. This is achieved by utilizing the Rodrigues parameterization to represent the rotation matrix. Both extensive simulated and real experiments are conducted to validate the proposed algorithm by comparing it with state-of-the-art registration methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by considering the anisotropic positional uncertainty into the rigid point set (PS) registration in the application of preoperative-to-intraoperative registration within image-guided surgery. We provide iterative solutions that compute the rotation and translation vector that aligns two 3-D PSs. The correspondences between points in two PSs are not known and regarded as hidden variables in the optimization process. Expectation–maximization technique is utilized to solve the maximum likelihood problem. We have demonstrated through experiments that our proposed approach can achieve lower registration error values than the compared state-of-the-art registration methods on various data sets. The readers should note that the proposed method is particularly suitable for cases that anisotropic noise is involved.

Usability test with medical personnel of a hand-gesture control techniques for surgical environment

Computer-assisted orthopedic surgery (CAOS) and augmented reality provides guidance for surgical environments in minimally invasive procedures. However, the surgeon’s interaction with those systems has limitations, because the surgical environment must be kept sterile. Two touch-less interaction techniques were evaluated within this research work, a vision-based technique used by META® glasses and electromyography-based used by MYO® arm-band. The research aim was to establish the most appropriate one touch-less interaction technique to manipulate a CAOS system. Usability was evaluated through an experiment with 4 orthopedic surgeons and 47 undergraduate students, in a simulated setting. The results suggest that both techniques can be relevant and useful in a surgical environment. However, the MYO was more efficient and possibly more effective to perform the manipulation tasks than META. In addition, the MYO arm-band was perceived as further satisfactory compared to META glasses and shown an improved overall behaviour in the CAOS system manipulations tasks.

Clinical use of computer‐assisted orthopedic surgery in horses

To describe clinical applications of computer-assisted orthopedic surgery (CAOS) in horses with a navigation system coupled with a cone beam computed tomography unit. Retrospective clinical case series. Thirteen adult horses surgically treated with CAOS. Medical records were searched for horses that underwent CAOS between 2016 and 2019. Data retrieved included signalment, diagnosis, lameness grade prior to surgery, surgical technique and complications, anesthesia and surgery time, and information pertaining to the perioperative case management and outcome. In 10 cases, surgical implants were placed in the proximal phalanx, third metatarsal bone, ulna, or medial femoral condyle. In one case, navigated transarticular drilling was performed to promote ankylosis of the distal tarsal joints. In another case, an articular fragment of the middle phalanx was removed with the help of CAOS guidance. In the final case, a focal osteolytic lesion of the calcaneal tuber was curetted with the aid of CAOS. In seven cases, a purpose-built frame was used for the surgical procedure. All surgeries were performed successfully and according to the preoperative plan. Computer-assisted orthopedic surgery can be an integral part of the clinical case management in equine surgery. To optimize workflow and time-efficiency, the authors recommend designating one team for operative planning and another for the execution of the surgical plan. Specialized equipment, such as the purpose-built frame, will further improve CAOS applications in equine surgery. After they have become familiar with the operational principles, equine surgeons can readily apply CAOS for a broad spectrum of indications.

Influence of a purpose-built frame on the accuracy of computer-assisted orthopedic surgery of equine extremities.

To determine the influence of a purpose-built frame on the accuracy of screw placement during computer-assisted orthopedic surgery (CAOS) of the equine extremity. Experimental cadaveric study. Twenty-four paired equine cadaveric limbs obtained from seven horses. Three 4.5-mm cortex screws were inserted in lag technique in three different planes of orientation in the proximal phalanx (P1) by means of CAOS. In the study group (n = 12 limbs), the tracker was anchored on a purpose-built frame designed to stabilize the extremity. In the control group (n = 12 limbs), a conventional tracker array was used that was anchored directly on P1. The stability of both tracker arrays was assessed during the procedure by using fiducial markers. After screw placement, preoperative and postoperative computed tomographic images were assessed to measure surgical accuracy aberrations (SAA) between the planned and achieved screw position. Descriptive statistics and repeated-measures analysis of variance were performed to compare SAA measurements between the study and control group. Both tracker arrays remained consistently stable in all specimens. Mean overall SAA of screw insertion were lower in the study group (0.7 mm; median, 0.5; range 0-3.4) than in the control group (1.2 mm; median, 0.9; range, 0-4.2 mm). The mean SAA achieved in cortex screw placement using CAOS lies within the range of approximately 1 mm. The use of a purpose-built frame avoided additional drilling of the target bone and improved surgical accuracy compared with the conventional tracker array. The purpose-built frame described in this report can be used to facilitate CAOS in equine orthopedics without compromising surgical accuracy.

A review of computer-assisted orthopaedic surgery systems.

Computer-assisted orthopaedic surgery systems have great potential, but no review has focused on computer-assisted surgery systems for the spine, hip, and knee. A systematic search was performed in Web of Science and PubMed. We searched the literature on computer-assisted orthopaedic surgery systems from 2008 to the present and focused on three aspects of systems: training, planning, and intraoperative navigation. In this review study, we reviewed 34 surgical training systems, 31 surgical planning systems, and 41 surgical navigation systems. The functions and characteristics of the surgical systems were compared and analysed, and the current concerns about and the impact of the surgical systems on doctors and surgery were clarified. Computer-assisted orthopaedic surgery systems are still in the development stage. Future surgical training systems should include synthetic models with patient anatomy. Surgical planning systems with automatic planning should be developed, and surgical navigation systems with multimodal fusion, robotic assistance and imaging should be developed.

Statistical Shape Modeling to Determine the Anterior Pelvic Plane for Total Hip Arthroplasty.

The anterior pelvic plane (APP) defined by both iliac spines and the pubic symphysis, is essential in total hip arthroplasty (THA) for the orientation of the prosthetic cup. However, the APP is nowadays still difficult to determine in computer assisted orthopedic surgery (CAOS). We propose to use a statistical shape model (SSM) of the pelvis to estimate the APP from ipsilateral anatomical landmarks, more easily accessible during surgery in computer assisted THA with the patient in lateral decubitus position. A SSM of the pelvis has been built from 40 male pelvises. Various ipsilateral anatomical landmarks have been extracted from these data and used to deform the SSM. Fitting the SSM to several combinations of these landmarks, we were able to reconstruct the pelvis with an accuracy between 2.8mm and 4.4mm, and estimate the APP inclination with an angular error between 1.3° and 2.8°, depending on the landmarks fitted. Results are promising and show that the APP could be acquired during the intervention from ipsilateral landmarks only.

Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN.

Automatic bone surfaces segmentation is one of the fundamental tasks of ultrasound (US)-guided computer-assisted orthopedic surgery procedures. However, due to various US imaging artifacts, manual operation of the transducer during acquisition, and different machine settings, many existing methods cannot deal with the large variations of the bone surface responses, in the collected data, without manual parameter selection. Even for fully automatic methods, such as deep learning-based methods, the problem of dataset bias causes networks to perform poorly on the US data that are different from the training set. In this work, an intensity-invariant convolutional neural network (CNN) architecture is proposed for robust segmentation of bone surfaces from US data obtained from two different US machines with varying acquisition settings. The proposed CNN takes US image as input and simultaneously generates two intermediate output images, denoted as local phase tensor (LPT) and global context tensor (GCT), from two branches which are invariant to intensity variations. LPT and GCT are fused to generate the final segmentation map. In the training process, the LPT network branch is supervised by precalculated ground truth without manual annotation. The proposed method is evaluated on 1227 in vivo US scans collected using two US machines, including a portable handheld ultrasound scanner, by scanning various bone surfaces from 28 volunteers. Validation of proposed method on both US machines not only shows statistically significant improvements in cross-machine segmentation of bone surfaces compared to state-of-the-art methods but also achieves a computation time of 30 milliseconds per image, [Formula: see text] improvement over state-of-the-art. The encouraging results obtained in this initial study suggest that the proposed method is promising enough for further evaluation. Future work will include extensive validation of the method on new US data collected from various machines using different acquisition settings. We will also evaluate the potential of using the segmented bone surfaces as an input to a point set-based registration method.

Accuracy and precision in resection alignment: Insights from 10,144 clinical cases using a contemporary computer-assisted total knee arthroplasty system

BackgroundStudies on total knee arthroplasty (TKA) with computer-assisted orthopedic surgery (CAOS) are limited by sample size or overlooked longitudinal performance of the system. This study aimed to assess resection accuracy across the entire TKA application history of a modern CAOS system considering multiple factors. MethodsA retrospective analysis was performed based on a database that archives technical logs of all TKAs performed using a CAOS system. Coronal resection errors and percentage of outliers (<2° alignment error) in the proximal tibia and distal femur were assessed. Multilevel modeling was used to understand whether and where the resection error variability was located in the grouping categories, which included geographic region, individual established surgeon, preoperative alignment, adoption phase (learning/proficient), and version of the CAOS software application. ResultsA total of 10,144 cases were reviewed. The accuracy (mean) and precision (standard deviation) of the coronal alignment for both the tibia and femur were at the sub-degree level. High percentages of acceptable resections were observed across the pooled and each grouping category. The accountability for the amounts of total variability in tibial and femoral resection errors was negligible for all grouping categories, demonstrated by ICC values less than the common variations in observational studies. ConclusionsThe study applied advanced analyses to assess alignment outcome in TKA bony resection alignment across the history of a specific CAOS system. The results demonstrated high resection alignment accuracy insensitive to geographic region, CAOS software application, adoption phase, preoperative alignment, and inter-surgeon differences.

Influence of multi-angle input of intraoperative fluoroscopic images on the spatial positioning accuracy of the C-arm calibration-based algorithm of a CAOS system.

Intraoperative fluoroscopic images, as one of the most important input data for computer-assisted orthopedic surgery (CAOS) systems, have a significant influence on the positioning accuracy of CAOS system. In this study, we proposed to use multi-angle intraoperative fluoroscopy images as input based on real clinical scenario, and the aim was to analyze the positioning accuracy and the error propagation rules with multi-angle input images compared with traditional two input images. In the experiment, the positioning accuracy of the C-arm calibration-based algorithm was studied, respectively, using two, three, four, five, and six intraoperative fluoroscopic images as input data. Moreover, the error propagation rules of the positioning error were analyzed by the Monte Carlo method. The experiment result showed that increasing the number of multi-angle input fluoroscopic images could reduce the positioning error of CAOS system, which has dropped from 1.01 to 0.61mm. The Monte Carlo simulation analysis showed that for random input errors subject to normal distribution (μ = 0, σ = 1), the image positioning error dropped from 0.29 to 0.23mm, and the staff gauge positioning error dropped from 1.36 to 1.19mm, while the tracking device positioning error dropped from 3.41 to 2.13mm. In addition, the results showed that image positioning error and staff gauge positioning error were all nonlinear error for the whole system, but tracker device positioning error was a strictly linear error. In conclusion, using multi-angle fluoroscopy images was helpful for clinic, which could improve the positioning accuracy of the CAOS system by nearly 30%. Graphical abstract The experiment process and Monte Carlo analysis of spatial positioning accuracy (A: Setup for the experiment; B: The process of Monte Carlo analysis; C: Results).

Automatic Markerless Registration and Tracking of the Bone for Computer-Assisted Orthopaedic Surgery

To achieve a simple and less invasive registration procedure in computer-assisted orthopaedic surgery, we propose an automatic, markerless registration and tracking method based on depth imaging and deep learning. A depth camera is used to continuously capture RGB and depth images of the exposed bone during surgery, and deep neural networks are trained to first localise the surgical target using the RGB image, then segment the target area of the corresponding depth image, from which the surface geometry of the target bone can be extracted. The extracted surface is then compared to a pre-operative model of the same bone for registration. This process can be performed dynamically during the procedure at a rate of 5-6 Hz, without any need for surgeon intervention or invasive optical markers. Ex vivo registration experiments were performed on a cadaveric knee, and accuracy measurements against an optically tracked ground truth resulted in a mean translational error of 2.74 mm and a mean rotational error of 6.66°. Our results are the first to describe a promising new way to achieve automatic markerless registration and tracking in computer-assisted orthopaedic surgery, demonstrating that truly seamless registration and tracking of the limb is within reach. Our method reduces invasiveness by removing the need for percutaneous markers. The surgeon is also exempted from inserting markers and collecting registration points manually, which contributes to a more efficient surgical workflow and shorter procedure time in the operating room.

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review.

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.

Computer assisted orthopaedic surgery: Past, present and future

Computer technology is ubiquitous and relied upon in virtually all professional activities including neurosurgery, which is why it is surprising that it is not the case for orthopaedic surgery with fewer than 5% of surgeons using available computer technology in their procedures. In this review, we explore the evolution and background of Computer Assisted Orthopaedic Surgery (CAOS), delving into the basic principles behind the technology and the changes in the discussion on the subject throughout the years and the impact these discussions had on the field. We found evidence that industry had an important role in driving the discussion at least in knee arthroplasty-a leading field of CAOS-with the ratio between patents and publications increased from approximately 1:10 in 2004 to almost 1:3 in 2014. The adoption of CAOS is largely restrained by economics and ergonomics with sceptics challenging the accuracy and precision of navigation during the early years of CAOS moving to patient functional improvements and long term survivorship. Nevertheless, the future of CAOS remains positive with the prospect of new technologies such as improvements in image-guided surgery, enhanced navigation systems, robotics and artificial intelligence.

Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN.

Ultrasound (US) provides real-time, two-/three-dimensional safe imaging. Due to these capabilities, it is considered a safe alternative to intra-operative fluoroscopy in various computer-assisted orthopedic surgery (CAOS) procedures. However, interpretation of the collected bone US data is difficult due to high levels of noise, various imaging artifacts, and bone surfaces response appearing several millimeters (mm) in thickness. For US-guided CAOS procedures, it is an essential objective to have a segmentation mechanism, that is both robust and computationally inexpensive. In this paper, we present our development of a convolutional neural network-based technique for segmentation of bone surfaces from in vivo US scans. The novelty of our proposed design is that it utilizes fusion of feature maps and employs multi-modal images to abate sensitivity to variations caused by imaging artifacts and low intensity bone boundaries. B-mode US images, and their corresponding local phase filtered images are used as multi-modal inputs for the proposed fusion network. Different fusion architectures are investigated for fusing the B-mode US image and the local phase features. The proposed methods was quantitatively and qualitatively evaluated on 546 in vivo scans by scanning 14 healthy subjects. We achieved an average F-score above 95% with an average bone surface localization error of 0.2mm. The reported results are statistically significant compared to state-of-the-art. Reported accurate and robust segmentation results make the proposed method promising in CAOS applications. Further extensive validations are required in order to fully understand the clinical utility of the proposed method.

The Use of Precision Alignment Technology to Circumvent Patient- Specific Roadblocks in Performing Total Knee Arthroplasty

The goal of total knee arthroplasty (TKA) is to provide the patient with a well-functioning, pain-free knee that will last for many years. To improve implant survival, the goal is to position the prosthesis in a way that restores proper biomechanical alignment, and advances in technology have enabled arthroplasty surgeons to produce accurate results on a more consistent basis. One of these advances is the use of computer-assisted orthopaedic surgery (CAOS) to assist in proper component alignment and produce accurate restoration of the biomechanical axis consistently.1–17 Functional scores and revision-free survival are at least equivalent to conventional arthroplasty.17–26 Traditional cutting guides for TKA rely on intramedullary femoral instruments, and either intramedullary or extramedullary tibial instruments, to obtain proper axial alignment. In cases where retained hardware is present, or in patients with knee osteoarthritis associated with pre-existing femoral or tibial extra-articular deformity, CAOS has proved to be an exceptionally useful, effective, and appealing option.27–37 A portable, accelerometer-based navigation device (OrthAlign, OrthAlign Inc., Aliso Viejo, CA) for TKA has demonstrated promising results with regard to the alignment accuracy of large-console CAOS systems.10–14 We present three patients who underwent TKA with retained femoral hardware. Two patients had intramedullary femoral fixation and one patient had an interference screw from a previous anterior cruciate ligament (ACL) reconstruction that would interfere with the use of a conventional intramedullary alignment guide. A novel, portable, accelerometer-based navigation (OrthAlign® precision alignment system, OrthAlign Inc., Aliso Viejo, CA) guide enabled performance of TKA without the need for surgical removal of hardware.

What is the best hip center location method to compute HKA angle in computer-assisted orthopedic surgery? In silico and in vitro comparison of four methods

BackgroundIn computer-assisted orthopedic surgery, the hip center (HC) can be determined by calculating the center of rotation of the femur in relation to the pelvis. Several methods are available: Gamage, Halvorsen, Pivot or Least-Moving Point (LMP). To our knowledge, no studies have compared these four methods. We therefore conducted in silico and in vitro experiments to assess whether their accuracy and precision in locating the HC and calculating the hip-knee-ankle (HKA) angle were equivalent. HypothesisThe four methods show similar accuracy and precision. Patients and methodsThe in silico experiment assessed the independent influence of four parameters (camera noise, acetabular noise, movement amplitude, and number of circumductions) on accuracy. The accuracy and precision of the four methods and the impact on HKA ankle calculation were assessed in an in vitro study on six cadaver limbs. ResultsIn the in silico experiment, all differences according to method were significant (p<0.0002). The Pivot method was the most accurate for acetabular and camera noise, number of circumductions, and movement amplitude. With the LMP, Pivot, Gamage and Halvorsen methods, error was respectively 23.07±8.40 (range 2.10–54.67) mm, 1.98±081 (0.15–4.89) mm, 28.18±3.42 (18.57–37.60) mm and 2.84±1.46 (0.11–9.44) mm depending on camera noise, 1.65±0.72 (0.13–4.80) mm, 0.52±0.22 (0.05–1.23) mm, 3.02±0.57 (0.60–4.78) mm and 0.61±0.27 (0.04–1.82) mm depending on movement amplitude, 0.50±0.20 (0.05–1.34) mm, 0.18±0.08 (0.01–0.44) mm, 0.36±0.14 (0.03–0.80) mm and 0.21±0.09 (0.01–0.55) mm depending on number of circumductions, and 11.30±5.77 (0.56–37.87) mm, 2.78±1.47 (0.10–8.77) mm, 88.08±8.85 (60.59–117.79) mm and 24.33±9.82 (1.40–66.17) mm depending on acetabular noise. In the in vitro experiment, differences were non-significant between the Pivot and LMP methods (p>0.98) and between the Gamage and Halvorsen methods (p>0.65). With the LMP, Pivot, Gamage and Halvorsen methods, precision was respectively 8.2±4.6 (3.3–23.6) mm, 7.3±3.6 (3.4–14.1) mm, 33.6±19.1 (4.7–111.4) mm and 35.0±25.0 (4.7–132.5) mm. Accuracy was 13.5±8.2 (3.2–40.7) mm. 12.3±6.4 (3.2–23.6) mm, 47.0±33.3 (6.2–176.6) mm and 40.3±27.8 (6.1–130.3) mm. The LMP and Pivot methods were thus more accurate and more precise than the Gamage and Halvorsen methods. HKA angle error was 1.1±0.9° (0.1–3.7) and 0.9±0.8° (0.0–2.5) with the LMP and Pivot methods, and 3.2±2.7° (0.0–12.7) and 3.8±3.5° (0.0–13.3) with the Gamage and Halvorsen methods. DiscussionThe study highlighted differences between the four methods of HC location in computer-assisted surgery; the Pivot method was the most accurate and precise, thus falsifying the study hypothesis. Level of evidenceIII, prospective comparative in silico and in vitro study.