Background Osteoarthritis affects about 528 million people worldwide, causing pain and stiffness in the joints. Arthroplasty is commonly performed to treat joint osteoarthritis, reducing pain and improving mobility. Nevertheless, a significant share of patients remain unsatisfied with their surgery. Personalised arthroplasty was introduced to improve surgical outcomes however current solutions require delays, making it difficult to integrate in clinical routine. We propose a fully automated workflow to design patient-specific implants for total knee arthroplasty. Methods The proposed pipeline first uses artificial neural networks to segment the femur and tibia proximal and distal extremities. Then the full bones are reconstructed using augmented statistical shape models, combining shape and landmarks information. Finally, 77 morphological parameters are computed to design patient-specific implants. The developed workflow has been trained on 91 CT scans and evaluated on 41 CT scans, in terms of accuracy and execution time. Results The workflow accuracy was 0.4±0.2mm for segmentation, 1.0±0.3mm for full bone reconstruction, and 2.2±1.5mm for anatomical landmarks determination. The custom implants fitted the patients’ anatomy with 0.9±0.5mm accuracy. The whole process from segmentation to implants’ design lasted about 15 minutes. Conclusion The proposed workflow allows the establishment of a patient-specific pre-operative planning in a very short time, making it easily available for all patients. It performs a fast and reliable personalisation of knee implants, including CT segmentation, bone modelling, morphological analysis and implant design, without requiring any manual intervention. Combined with efficient implant manufacturing techniques, this solution could help answer the growing number of arthroplasties while reducing complications and improving patients’ satisfaction.

Plane wave imaging persists as a focal point of research due to its high frame rate and low complexity. However, in spite of these advantages, its performance can be compromised by several factors such as noise, speckle, and artifacts that affect the image quality and resolution. In this paper, we propose an attention-based complex convolutional residual U-Net to reconstruct improved in-phase/quadrature complex data from a single insonification acquisition that matches diverging wave imaging. Our approach introduces an attention mechanism to the complex domain in conjunction with complex convolution to incorporate phase information and improve the image quality matching images obtained using coherent compounding imaging. To validate the effectiveness of this method, we trained our network on a simulated phased array dataset and evaluated it using in vitro and in vivo data. The experimental results show that our approach improved the ultrasound image quality by focusing the network’s attention on critical aspects of the complex data to identify and separate different regions of interest from background noise.

The usual Lewinnek orientation for cup positioning in total hip arthroplasty is not suitable for all patients as it does not consider the patient mobility. We propose an ultrasound‐based approach to compute a Functional Safe Zone (FSZ) considering daily positions. Our goal was to validate it, and to evaluate how the input parameters impact the FSZ size and barycentre. The accuracy of the FSZ was first assessed by comparing the FSZ computed by the proposed approach and the true FSZ determined by 3D modelling. Then, the input parameters' impact on the FSZ was studied using a principal component analysis. The FSZ was estimated with errors below 0.5° for mean anteversion, mean inclination, and at edges. The pelvic tilts and the neck orientation were found correlated to the FSZ mean orientation, and the target ROM and the prosthesis dimensions to the FSZ size. Integrated into the clinical workflow, this non‐ionising approach can be used to easily determine an optimal patient‐specific cup orientation minimising the risks of dislocation.

SterEOS is a software developed by EOS Imaging® allowing the measurement of 3D orthopaedic parameters on two bi-planar radiographs. The goal of this preliminary study was to assess the reliability of the pelvic measurements. Two observers, a novice and an intermediate user, measured three times these parameters on pre and postoperative EOS images coming from ten patients. Intra- and inter-observer precision have been evaluated with intra-class coefficient (ICC) and Bland-Altman graphs. On preoperative EOS images, a high intra- and inter-observer precision (ICC>0.8) was obtained for the measurement of the femoral head diameter, the femur length, the pelvic version and the pelvic obliquity. The offset, the femoral neck length, the pelvic incidence and the sacral slope measurement had a high intra-observer precision but a lower inter-observer precision. The measurement of the acetabulum inclination and anteversion, the CCD angle, the femur torsion, the pelvic rotation and the anterior pelvic plane inclination had a low intra- and inter-observer precision. Similar results were found on postoperative EOS images. Our results are partially consistent with the literature since authors found high intra- and inter-observer precision for all pelvic parameters. Further studies are therefore needed to evaluate the impact of the observer experience on the reliability of those measurements.