Segmentation accuracy.

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 for segmentation, for full bone reconstruction, and for anatomical landmarks determination. The custom implants fitted the patients’ anatomy with 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.