ArticlePloS one2025
Fully automated workflow for designing patient-specific orthopaedic implants: Application to total knee arthroplasty.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of artificial intelligence in the design of highly compatible knee prostheses: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- Article
- Artificial intelligence and multimodal imaging in orthopaedics: from technological advances to clinical translation.Frontiers in medicine · 2025Review
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Authors and funding
9 authors.
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Abstract
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 [Formula: see text] for segmentation, [Formula: see text] for full bone reconstruction, and [Formula: see text] for anatomical landmarks determination. The custom implants fitted the patients' anatomy with [Formula: see text] 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.
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