Robotic and navigated TKA procedures have been introduced to improve component placement precision for the purpose of improving implant survivorship and other clinical outcomes. Although numerous studies have shown enhanced precision in placing components, adoption of technology-assistance (TA) for TKA has been relatively slow. One reason for this has been the difficulty in demonstrating the cost-effectiveness of implementing TA-TKA systems and assessing their impact on revision rates.

In this study, we aimed to use a simulation approach to answer the following questions: (1) Can we determine the distribution of likely reductions in TKA revision rates attributable to TA-TKA in an average US patient population? And, (2) What reduction in TKA revision rates are required to achieve economic neutrality?

In a previous study, we developed a method for creating large sets of simulated TKA patient populations with distributions of patient-specific factors (age at index surgery, sex, BMI) and one surgeon-controlled factor (coronal alignment) drawn from registry data and published literature. Effect sizes of each factor on implant survival was modeled using large clinical studies. For 10,000 simulated TKA patients, we simulated 20,000 TKA surgeries, evenly split between groups representing coronal alignment precisions reported for manual (±3°) and TA-TKA (±1.0°), calculating the patient-specific survival curve for each group. Extending our previous study, we incorporated the probability of each patient's expected survival into our model using publicly available actuarial data. This allowed us to calculate a patient-specific estimate of the Reduction in Lifetime Risk of Revision (RLRR) for each simulated patient. Our analysis showed that 90% of patients will achieve an RLRRof 1.5% or less in an average US TKA population.

We then conducted a simplified economic analysis with the goal of determining the net cost of using TA-TKA per case when factoring in future savings by TKA revision rates. We assumed an average cost of revision surgery to be $75,000 as reported by Delanois (2017) and an average added cost incurred by TA-TKA to be $6,000 per case as reported by Antonis (2019). We estimate the net cost per TA-TKA case (C_Net) to be the added cost per TA-TKA intervention (C_Int), less the cost of revision surgery (C_Rev) multiplied by the estimated RLRR: C_Net = C_Int - C_Rev∗RLRR. We find that, under these assumptions, use of TA-TKA increases expected costs for all patients with an RLRR of under 8%.

Based on these results, it appears that it would not be cost-effective to use TA-TKA on more than a small fraction of the typical US TKA patient population if the goal is to reduce overall costs through reducing revision risks. However, we note that this simulation does not consider other possible reported benefits of TA-TKA surgery, such as improved functional and pain outcome scores which may justify its use on other grounds. Alternative costs incurred by TA-TKA will be evaluated in a future study. To reach economic neutrality, TA-TKA systems either must reduce the added cost per intervention or increase RLRR by better addressing the root causes of revision.

Introduction

Innovations in surgical robotics and navigation have significantly improved implant placement accuracy in total knee arthroplasty (TKA). However, many comparative studies have not been shown to substantially improve revision rates or other clinical outcome scores. We conducted a simulation study based on the reported distribution of patient-specific characteristics and estimated potential effect of coronal plane alignment (CPA) on risk of revision to evaluate the hypothesis that most published study designs in this area have been too underpowered to detect improvements in revision rates.

Dynamic 2D sonography of the infant hip is a commonly used clinical procedure for developmental dysplasia of the hip (DDH) screening. It however has been found to be unreliable with some studies reporting associated misdiagnosis rates of up to 29%. In a recent systematic review, Charlton et al. examined dynamic ultrasound (US) screening for hip instability in the first six weeks after birth and found current best practices for such early screening techniques to be divergent between international institutions in terms of clinical scanning protocols. Such protocols include: the appropriate scanning plane and US probe position (e.g. coronal, transverse, lateral, anterior), DDH diagnostic metrics (e.g. femoral head coverage, alpha angle), appropriate patient age when scanning, and follow up procedures. To improve reliability of diagnosis and to help in standardizing diagnosis across different raters and health-centers, we propose an automated method for dynamically assessing hip instability using 3D US.

38 infant hips from 19 patients were scanned with B-mode 3D US by a paediatric orthopaedic surgeon and two technologists from the radiology department at a paediatric tertiary care centre. To quantify hip assessment, we proposed the use of femoral head coverage variability (ΔFHC3D) within 3D US volumes collected during a sequence of US scans (one at rest, and another with posterior stress applied to the joint as maneuvered during a dynamic assessment). We used phase symmetry image features to localize the ilium's vertical cortex and a random forest classifier to identify the location of the femoral head.

The proposed ΔFHC3D provided good repeatability with an average test-retest ICC measure of 0.70 (95% confidence interval: 0.35 to 0.87, F(21,21) = 7.738, p<.001). The mean difference of ΔFHC3D measurements was 0.61% with a SD of 4.05%.

Since the observed changes in ΔFHC3D start near 0% and range up to about 18% from stable to mildly unstable hips in this cohort, the mean difference and standard deviation of ΔFHC3D measurements observed suggest that the proposed metric and technique likely have sufficient resolution and repeatability to quantify differences in hip laxity. The long-term significance of this approach to evaluating dynamic assessments may lie in increasing early diagnostic accuracy in order to prevent dysplasia remaining undetected prior to manifesting itself in early adulthood joint disease.

Fluoroscopic C-arms are operated by medical radiography technologists (RTs) in Canadian operating rooms (ORs). While they do receive formal, accredited training, most of it is theoretical, rather than hands-on. During their first encounters in the OR, new RTs can experience difficulty achieving the radiographic views required by surgeons, often needing several scout X-rays during C-arm positioning. Furthermore, ambiguous language by surgeons often inadequately conveys their request. The result is often frustration, unnecessary radiation exposure, and added OR time. The purpose of this study was to evaluate the value of artificial X-rays in improving C-arm positioning performance, with inexperienced C-arm users.

We developed an Artificial X-ray Imaging System (AXIS) that generates Digitally Reconstructed Radiographs (DRRs), or artificial X-ray images, based on the relative position of a C-arm and manikin. 30 participants were enrolled in the user study and performed four activities: an introduction session, an AXIS-guided evaluation, a non-AXIS-guided evaluation, and a questionnaire. The main goal of the study was to assess C-arm positioning performance with and without AXIS guidance. For each evaluation, the participants had to replicate a set of target X-ray images by taking real radiographs of the manikin with the C-arm. During the AXIS evaluation, artificial X-rays were generated at 2 Hz for guidance, while in the non-AXIS evaluation, the participants had to acquire real scout X-rays to guide them toward the correct view.

For each imaging task the number of real X-rays and time required per task was recorded, and the C-arm's pose was tracked and compared to the target pose to determine positioning accuracy; these were averaged for each participant and condition. Hypothesis testing on the means and paired t-tests were carried out using a significance level of α=0.05.

On average, users took significantly fewer real scout X-ray images (53% fewer (2.8 vs 6.0), p<0.001) when guided by AXIS. Lateral distance accuracy was improved by 10% for final C- arm positions and by 26% for the most accurate intermediate C-arm positions when guided by AXIS (p<0.05). There was no significant difference in average task time or angular accuracies between the AXIS and non-AXIS evaluations. Overall, we are encouraged by these findings and plan to further develop this system with the goal of deploying it both for training and intraoperative uses.

Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic imaging that is limited to two- dimensions and is associated to low accuracies. We have previously introduced a full-dimensional position assessment framework based on registering intraoperative X-rays to preoperative volumetric images with sufficient accuracies. However, the framework requires a semi-manual process of pedicle screw segmentation and the intraoperative X-rays have to be taken from defined positions in space in order to avoid pedicle screws' head occlusion. This motivated us to develop advancements to the system to achieve higher levels of automation in the hope of higher clinical feasibility.

In this study, we developed an automatic segmentation and X-ray adequacy assessment protocol. An artificial neural network was trained on a dataset that included a number of digitally reconstructed radiographs representing pedicle screw projections from different points of view. This model was able to segment the projection of any pedicle screw given an X-ray as its input with accuracy of 93% of the pixels. Once the pedicle screw was segmented, a number of descriptive geometric features were extracted from the isolated blob. These segmented images were manually labels as ‘adequate’ or ‘not adequate’ depending on the visibility of the screw axis. The extracted features along with their corresponding labels were used to train a decision tree model that could classify each X-ray based on its adequacy with accuracies on the order of 95%.

In conclusion, we presented here a robust, fast and automated pedicle screw segmentation process, combined with an accurate and automatic algorithm for classifying views of pedicle screws as adequate or not. These tools represent a useful step towards full automation of our pedicle screw positioning assessment system.

Ultrasound (US) is the standard imaging modality used to screen for developmental dysplasia of the hip in infants. Currently, radiologists or orthopaedic surgeons review scan images and judge them to be adequate or inadequate for interpretation. If considered adequate, diagnostic dysplasia metrics are determined; however, there is no standardised method for this process. There is significant inter-observer variability in this manual process which can affect misdiagnosis rates. To eliminate this subjectivity, we developed an automatic method to identify adequate US images and extract dysplasia metrics. The purpose of this study was to validate the efficacy of this automatic method by comparing results with observer-determined dysplasia metrics on a set of US images.

A total of 693 US images from scans of 35 infants were analysed. Trained clinicians at a single institution labeled each image as adequate or inadequate, and subsequently measured alpha and beta angles on adequate images to diagnose dysplasia. We trained our image classifier on random sets of 415 images and used it to assess the remaining 278 images. Alpha and beta angles were automatically estimated on all adequate images. We compared the manual and automatic methods for discrepancies in adequacy determination, metric variability and incidences of missed early diagnosis or over-treatment.

There was excellent agreement between the automatic and manual methods in image adequacy classification (Kappa coefficient = 0.912). On each adequate US image, alpha and beta angle measurements were compared, producing mixed levels of agreement between methods. Mean discrepancies of 1.78°±4.72° and 8.91°±6.437° were seen for alpha and beta angles, respectively. Standard deviations of the angle measures across multiple images from a single patient scan were significantly reduced by the automatic method for both alpha (p<0.05) and beta (p<0.01) angles. Additionally, the automatic method classified three hips (two patients) as Graf type II and two hips (two patients) as type III, while the manual method classified them as type I and II, respectively. Both cases flagged as type III patients by the automatic system subsequently failed Pavlik harness treatment and were booked for surgery.

The automatic method produced excellent agreement with radiologists in scan adequacy classification and significantly reduced measurement variability. Good agreement between methods was found in Graf classification. In instances of disagreement, subsequent clinical findings seemed to support the classification of the automatic method. This proposed method presents an alternative automatic, near-real-time analysis for US images that may potentially significantly improve dysplasia metric reliability and reduce missed early diagnoses without increasing over-treatment.

Despite being demonstrably better than conventional surgical techniques with regards to implant alignment and outlier reduction, computer navigation systems have not faced widespread adoption in surgical operating rooms. We believe that one of the reasons for the low uptake stems from the bulky design of the optical tracker assemblies. These trackers must be rigidly fixed to a patient's bone and they occupy a significant portion of the surgical workspace, which makes them difficult to use. In this study we introduce the design for a new optical tracker system, and subsequently we evaluate the tracker's performance. The novel tracker consists of a set of low-profile flexible pins that can be placed into a rigid body and individually deflect without greatly affecting the pose estimation. By relying on a pin's stiff axial direction while neglecting lateral deviations, we can gain sufficient constraint over the underlying body. We used an unscented Kalman filter based algorithm as a recursive body pose estimator that can account for relative marker displacements. We assessed our tracker's performance through a series of simulations and experiments inspired by a total knee arthroplasty. We found that the flexible tracker performs comparably to conventional trackers with regards to accuracy and precision, with tracking errors under 0.3mm for typical operating conditions. The tracking error remained below 0.5mm during pin deflections of up to 40mm. Our algorithm ran at computation speeds greater than real-time at 30Hz which means that it would be suitable for use in real-time applications. We conclude that this flexible pin concept provides sufficient accuracy to be used as a replacement for rigid trackers in applications where its lower profile, its reduced invasiveness and its robustness to deflection are desirable characteristics.

Ultrasound (US) imaging is recommended for early detection of Developmental Dysplasia of the Hip (DDH) to guide decisions about possible surgical treatment. However, a number of studies have raised concerns over the efficacy of US in early diagnosis. The main limitation of US-based diagnosis is sub-standard reliability of the primary dysplasia metric measurements: namely, the alpha and beta angles. In this study, we have proposed a novel and automatic method to extract dysplasia metrics from 2D US, which we hope will improve the overall reliability of US-based DDH measurements by removing error due to subjective measurements. We hypothesise that improvements in reliability of dysplasia metric measurements will reduce the chances of missed early-diagnosis, and therefore reduce the need for later complex surgical treatments.

We evaluated performance of the algorithm on 4 infants diagnosed with US scans for DDH. The typical runtime of our algorithm is less than 1 second for an US image. We found a 6° bias between manual and automatic measurements, with automatic measurements tending to be lower in value; the standard deviation in the discrepancy values was also relatively high at 7°. This suggests that there is considerable variability in the angle estimation process, which is typically done manually, which supports our contention that further work needs to be done to establish an accurate and repeatable measurement technique. Further, we found agreements in the Graf-classification types in six out of seven sessions. For the one patient where there was a discrepancy in classification, later US sessions suggest the manual technique possibly missed the opportunity for early detection, in contrast to the automatic method which classified the patient as having evidence of dysplasia. Thus, such an automatic method may improve the reliability of current US-based DDH diagnosis techniques. The primary limitation of this study is that we have done strictly an intra-image discrepancy analysis and have not compared the results with what could be considered a ‘gold standard’ reference. In future work, we plan to assess these indices on 3D images of the hip and assess the accuracy of proposed 2D and 3D-based automatic index calculation techniques against a 3D reference model.

Background

Previously, we demonstrated the effectiveness of phase symmetry (PS) features for segmentation and localisation of bone fractures in 3D ultrasound for the purpose of orthopedic fracture reduction surgery. We recently proposed a novel real-time image-processing method of bone surface extraction from local phase features of clinical 3D B-mode ultrasound data. We are presenting a computational study and outline planned future developments for integration into a computer aided orthopedic surgery framework.

Introduction

Bracing, a strategy employed by humans and robotic devices, can be generally described as a parallel mechanical link between the actor, the environment, and/or the workpiece that alters the mechanical impedance between the tool and workpiece in order to improve task performance. In this study we investigated the potential value of bracing in the context of bone milling to treat cam-type femoroacetabular impingement (FAI) lesions. The goal of this study was to evaluate whether a proposed bracing technique could enable a user to perform a cam resection more accurately and quickly than a currently employed arthroscopic technique.

Due to its ease of use, portability, low cost, real-time response and absence of ionising radiation, ultrasound (US) imaging could potentially be an important tool for non-invasive diagnostic imaging in orthopaedics. Unfortunately, nonlinear characteristics of ultrasound, low signal-to-noise ratio and speckle make it difficult to accurately and reliably determine the location and shape of the bone surface. Recently, local phase-based image processing methods, named phase symmetry (PS), have been shown to perform very well at locating bone surfaces in ultrasound images, with reported accuracies of better than 0.4mm. The local phase features are extracted by filtering the B-mode US image in the frequency domain with a Log-Gabor filter. Although successful results were achieved, accurate localization is highly affected by the choice of filter parameters. Recently, our group proposed a method of automatically selecting the scale, bandwidth and orientation parameters of Log-Gabor filters. Previously, we showed our first clinical results using local phase information to identify distal radius fractures from B-mode US images using automatically selected filter parameters.

The objective of the current study was to determine if the proposed automatic parameter selection method could produce accurate pelvic bone surface shapes in a live clinical setting.

CT scans were obtained as part of normal clinical care from ten patients admitted to Vancouver General Hospital for pelvic fractures. A ‘gold standard’ bone surface was computed from the CT scan. After obtaining informed consent, we performed an additional US scan using a commercially-available real-time scanner (Voluson 730, GE Healthcare, Waukesha, WI) with a 3D US transducer. The PS bone surfaces were extracted from the US scans using the empirical Log-Gabor filter parameters and optimised Log-Gabor filter parameters. The bone surfaces on CT were extracted using a standard thresholding approach that minimises the intra-class variance. The US images were then registered to the CT images using a feature-based rigid registration algorithm with manual landmarking. The quality of the resulting surface matching was evaluated by computing the root mean square distance between the two surface representations.

The average fiducial registration error was 0.31mm (SD 0.25mm). The average surface fitting error (SFE) was 0.72mm (SD 1.24 mm) for PS surfaces extracted using empirical filter parameters and 0.41mm (SD 0.44 mm) using the optimized filter parameters.

In this study, we have demonstrated that our automatic filter parameter selection process can be applied successfully to a bone surface extraction task on 3D US images acquired under clinically realistic conditions. The accuracy of the resulting bone surface is excellent, with an average discrepancy relative to a CT standard of well under a millimeter. This level of accuracy is likely to be sufficiently good for a number of important surgical tasks, including CT to US registration.

Purpose

Radiographs are the most common imaging modality used to guide orthopaedic interventions. Ultrasound (US) imaging offers potential advantages for intraoperative imaging by its portability and ability to produce real-time 2D or 3D images without radiation to either the patient or surgical team. Our objective in this study was to determine in a live emergency room setting, if a newly-developed image processing method for 3D US would allow us to accurately extract (reproduce) the surfaces of fractured bones.