Aims. Robotic-assisted total knee arthroplasty (RA-TKA) is theoretically more accurate for component positioning than TKA performed with mechanical instruments (M-TKA). Furthermore, the ability to incorporate soft-tissue laxity data into the plan prior to bone resection should reduce variability between the planned polyethylene thickness and the final implanted polyethylene. The purpose of this study was to compare
The object of this study was to develop a method to assess the
We conducted a meta-analysis, including randomised
controlled trials (RCTs) and cohort studies, to examine the effect
of patient-specific instruments (PSI) on radiological outcomes after
total knee replacement (TKR) including: mechanical axis alignment
and malalignment of the femoral and tibial components in the coronal,
sagittal and axial planes, at a threshold of >
3º from neutral.
Relative risks (RR) for malalignment were determined for all studies
and for RCTs and cohort studies separately. Of 325 studies initially identified, 16 met the eligibility criteria,
including eight RCTs and eight cohort studies. There was no significant
difference in the likelihood of mechanical axis malalignment with
PSI versus conventional TKR across all studies
(RR = 0.84, p = 0.304), in the RCTs (RR = 1.14, p = 0.445) or in
the cohort studies (RR = 0.70, p = 0.289). The results for the alignment
of the tibial component were significantly worse using PSI TKR than conventional
TKR in the coronal and sagittal planes (RR = 1.75, p = 0.028; and
RR = 1.34, p = 0.019, respectively, on pooled analysis). PSI TKR
showed a significant advantage over conventional TKR for alignment
of the femoral component in the coronal plane (RR = 0.65, p = 0.028
on pooled analysis), but not in the sagittal plane (RR = 1.12, p =
0.437). Axial alignment of the tibial (p = 0.460) and femoral components
(p = 0.127) was not significantly different. We conclude that PSI does not improve the
Objective . Dunkin Hartley guinea pigs, a commonly used animal model of osteoarthritis,
were used to determine if high frequency ultrasound can ensure intra-articular
injections are accurately positioned in the knee joint. Methods. A high-resolution small animal ultrasound system with a 40 MHz
transducer was used for image-guided injections. A total of 36 guinea
pigs were anaesthetised with isoflurane and placed on a heated stage.
Sterile needles were inserted directly into the knee joint medially,
while the transducer was placed on the lateral surface, allowing
the femur, tibia and fat pad to be visualised in the images. B-mode
cine loops were acquired during 100 µl. We assessed our ability
to visualise 1) important anatomical landmarks, 2) the needle and
3) anatomical changes due to the injection. . Results. From the ultrasound images, we were able to visualise clearly
the movement of anatomical landmarks in 75% of the injections. The
majority of these showed separation of the fat pad (67.1%), suggesting
the injections were correctly delivered in the joint space. We also
observed dorsal joint expansion (23%) and patellar tendon movement
(10%) in a smaller subset of injections. Conclusion. The results demonstrate that this image-guided technique can
be used to visualise the location of an intra-articular injection
in the joints of guinea pigs. Future studies using an ultrasound-guided
approach could help improve the injection
We aimed to determine the reliability,
Our study evaluated the
The diagnosis of a meniscal tear may require MRI, which is costly. Ultrasonography has been used to image the meniscus, but there are no reliable data on its
We performed A total of 12 cadaveric lower limbs were tested with a commercial
image-free navigation system using trackers secured by bone screws.
We then tested a non-invasive fabric-strap system. The lower limb
was secured at 10° intervals from 0° to 60° of knee flexion and
100 N of force was applied perpendicular to the tibia. Acceptable
coefficient of repeatability (CR) and limits of agreement (LOA)
of 3 mm were set based on diagnostic criteria for anterior cruciate
ligament (ACL) insufficiency.Objectives
Methods
Aims. The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited sensitivity and specificity. Recent research has suggested using blood cell ratios, such as platelet-to-volume ratio (PVR) and platelet-to-lymphocyte ratio (PLR), to improve diagnostic
Aims. The diagnosis of periprosthetic joint infection (PJI) continues to present a significant clinical challenge. New biomarkers have been proposed to support clinical decision-making; among them, synovial fluid alpha-defensin has gained interest. Current research methodology suggests reference methods are needed to establish solid evidence for use of the test. This prospective study aims to evaluate the diagnostic
Aims. The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model
Aims. We aimed to assess the reliability and validity of OpenPose, a posture estimation algorithm, for measurement of knee range of motion after total knee arthroplasty (TKA), in comparison to radiography and goniometry. Methods. In this prospective observational study, we analyzed 35 primary TKAs (24 patients) for knee osteoarthritis. We measured the knee angles in flexion and extension using OpenPose, radiography, and goniometry. We assessed the test-retest reliability of each method using intraclass correlation coefficient (1,1). We evaluated the ability to estimate other measurement values from the OpenPose value using linear regression analysis. We used intraclass correlation coefficients (2,1) and Bland–Altman analyses to evaluate the agreement and error between radiography and the other measurements. Results. OpenPose had excellent test-retest reliability (intraclass correlation coefficient (1,1) = 1.000). The R. 2. of all regression models indicated large correlations (0.747 to 0.927). In the flexion position, the intraclass correlation coefficients (2,1) of OpenPose indicated excellent agreement (0.953) with radiography. In the extension position, the intraclass correlation coefficients (2,1) indicated good agreement of OpenPose and radiography (0.815) and moderate agreement of goniometry with radiography (0.593). OpenPose had no systematic error in the flexion position, and a 2.3° fixed error in the extension position, compared to radiography. Conclusion. OpenPose is a reliable and valid tool for measuring flexion and extension positions after TKA. It has better
Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent
Aims. The primary aim of this study was to compare the postoperative systemic inflammatory response in conventional jig-based total knee arthroplasty (conventional TKA) versus robotic-arm assisted total knee arthroplasty (robotic TKA). Secondary aims were to compare the macroscopic soft tissue injury, femoral and tibial bone trauma, localized thermal response, and the
Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the
Aims. The aims of this study were: 1) to describe extended restricted kinematic alignment (E-rKA), a novel alignment strategy during robotic-assisted total knee arthroplasty (RA-TKA); 2) to compare residual medial compartment tightness following virtual surgical planning during RA-TKA using mechanical alignment (MA) and E-rKA, in the same set of osteoarthritic varus knees; 3) to assess the requirement of soft-tissue releases during RA-TKA using E-rKA; and 4) to compare the
Aims. The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results. The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an
Limb alignment in total knee arthroplasty (TKA) influences periarticular soft-tissue tension, biomechanics through knee flexion, and implant survival. Despite this, there is no uniform consensus on the optimal alignment technique for TKA. Neutral mechanical alignment facilitates knee flexion and symmetrical component wear but forces the limb into an unnatural position that alters native knee kinematics through the arc of knee flexion. Kinematic alignment aims to restore native limb alignment, but the safe ranges with this technique remain uncertain and the effects of this alignment technique on component survivorship remain unknown. Anatomical alignment aims to restore predisease limb alignment and knee geometry, but existing studies using this technique are based on cadaveric specimens or clinical trials with limited follow-up times. Functional alignment aims to restore the native plane and obliquity of the joint by manipulating implant positioning while limiting soft tissue releases, but the results of high-quality studies with long-term outcomes are still awaited. The drawbacks of existing studies on alignment include the use of surgical techniques with limited
Aims. No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the
Aims. The purpose of this study was to compare the radiological outcomes of manual versus robotic-assisted medial unicompartmental knee arthroplasty (UKA). Methods. Postoperative radiological outcomes from 86 consecutive robotic-assisted UKAs (RAUKA group) from a single academic centre were retrospectively reviewed and compared to 253 manual UKAs (MUKA group) drawn from a prior study at our institution. Femoral coronal and sagittal angles (FCA, FSA), tibial coronal and sagittal angles (TCA, TSA), and implant overhang were radiologically measured to identify outliers. Results. When assessing the