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 accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results. The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion. We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients. Cite this article: Bone Jt Open 2024;5(2):101–108

Abstract. Introduction. In recent years, CTA has been an effective training adjunct for orthopaedic procedures. ACLR is a complex procedure with a steep learning curve. Aim. To design a multimedia CTA learning tool for ACLR using a modified Delphi methodology. Methodology:. CTA generation: A modified Delphi method was used to generate a list of Technical Steps (TS), Decision Points (DP) and errors/solutions for an ACLR that was approved by an expert consensus amongst four, fellowship-trained knee surgeons. A technical workflow was designed to generate the audio-visual tool. Pilot study: 30 novice trainees were granted access to the CTA tool and then performed the ACLR in a cadaveric simulation workshop. A 5-point Likert scale aimed to qualitatively assess if the CTA was useful to their learning. Results:. CTA generation: 156 TS and 79 DP were included for an ACLR procedure which was divided into 7 phases. The audio-visual component was combined with the written tool to design the final CTA tool. Pilot study: 95% of trainees suggested the CTA tool would be useful before carrying out an ACLR in the operating theatre. They agreed it made their ACLR learning more effective. Conclusion:. We have designed a novel and easily accessible CTA tool to standardise teaching of ACLR surgery. Novice surgeons verify the value of the CTA as an adjunct to their apprenticeship training. Future work: A Randomised Controlled Trial (RCT) is currently being planned to evaluate whether there are any objective benefits of this CTA to train surgeons performing an ACLR

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

Aims

The objective of this study is to assess the use of ultrasound (US) as a radiation-free imaging modality to reconstruct 3D anatomy of the knee for use in preoperative templating in knee arthroplasty.

Aims

Neither a surgeon’s intraoperative impression nor the parameters of computer navigation have been shown to be predictive of the outcomes following total knee arthroplasty (TKA). The aim of this study was to determine whether a surgeon, with robotic assistance, can predict the outcome as assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS) for pain (KPS), one year postoperatively, and establish what factors correlate with poor KOOS scores in a well-aligned and balanced TKA.

Aims

The mobile bearing Oxford unicompartmental knee arthroplasty (OUKA) is recommended to be performed with the leg in the hanging leg (HL) position, and the thigh placed in a stirrup. This comparative cadaveric study assesses implant positioning and intraoperative kinematics of OUKA implanted either in the HL position or in the supine leg (SL) position.

Aim

There has been a significant reduction in unicompartmental knee arthroplasty (UKA) procedures recorded in Australia. This follows several national joint registry studies documenting high UKA revision rates when compared to total knee arthroplasty (TKA). With the recent introduction of robotically assisted UKA procedures, it is hoped that outcomes improve. This study examines the cumulative revision rate of UKA procedures implanted with a newly introduced robotic system and compares the results to one of the best performing non-robotically assisted UKA prostheses, as well as all other non-robotically assisted UKA procedures.

Objectives

The use of the haptically bounded saw blades in robotic-assisted total knee arthroplasty (RTKA) can potentially help to limit surrounding soft-tissue injuries. However, there are limited data characterizing these injuries for cruciate-retaining (CR) TKA with the use of this technique. The objective of this cadaver study was to compare the extent of soft-tissue damage sustained through a robotic-assisted, haptically guided TKA (RATKA) versus a manual TKA (MTKA) approach.

Aims

Patient-specific instrumentation of total knee arthroplasty (TKA) is a technique permitting the targeting of individual kinematic alignment, but deviation from a neutral mechanical axis may have implications on implant fixation and therefore survivorship. The primary objective of this randomized controlled study was to compare the fixation of tibial components implanted with patient-specific instrumentation targeting kinematic alignment (KA+PSI) versus components placed using computer-assisted surgery targeting neutral mechanical alignment (MA+CAS). Tibial component migration measured by radiostereometric analysis was the primary outcome measure (compared longitudinally between groups and to published acceptable thresholds). Secondary outcome measures were inducible displacement after one year and patient-reported outcome measures (PROMS) over two years. The secondary objective was to assess the relationship between alignment and both tibial component migration and inducible displacement.

Objectives

Static radiostereometric analysis (RSA) using implanted markers is considered the most accurate system for the evaluation of prosthesis migration. By using CT bone models instead of markers, combined with a dynamic RSA system, a non-invasive measurement of joint movement is enabled. This method is more accurate than current 3D skin marker-based tracking systems. The purpose of this study was to evaluate the accuracy of the CT model method for measuring knee joint kinematics in static and dynamic RSA using the marker method as the benchmark.

We retrospectively reviewed the records of 1150 computer-assisted total knee replacements and analysed the clinical and radiological outcomes of 45 knees that had arthritis with a pre-operative recurvatum deformity. The mean pre-operative hyperextension deformity of 11° (6° to 15°), as measured by navigation at the start of the operation, improved to a mean flexion deformity of 3.1° (0° to 7°) post-operatively. A total of 41 knees (91%) were managed using inserts ≤ 12.5 mm thick, and none had mediolateral laxity > 2 mm from a mechanical axis of 0° at the end of the surgery. At a mean follow-up of 26.4 months (13 to 48) there was significant improvement in the mean Knee Society, Oxford knee and Western Ontario and McMaster Universities Osteoarthritis Index scores compared with the pre-operative values. The mean knee flexion improved from 105° (80° to 125°) pre-operatively to 131° (120° to 145°), and none of the limbs had recurrent recurvatum.

These early results show that total knee replacement using computer navigation and an algorithmic approach for arthritic knees with a recurvatum deformity can give excellent radiological and functional outcomes without recurrent deformity.