Arthroplasty is commonly performed to treat joint osteoarthritis, reducing
pain and improving mobility. While arthroplasty has known several technical
improvements, a significant share of patients are still unsatisfied with their
surgery. Personalised arthroplasty improves 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,
presented for total knee arthroplasty, the most widely performed arthroplasty
in the world nowadays.
The proposed pipeline first uses artificial neural networks to segment the
proximal and distal extremities of the femur and tibia. 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 using
91 CT scans of lower limb and evaluated on 41 CT scans manually segmented, in
terms of accuracy and execution time.
The workflow accuracy was $0.4\pm0.2mm$ for the segmentation, $1.2\pm0.4mm$
for the full bones reconstruction, and $2.8\pm2.2mm$ for the anatomical
landmarks determination. The custom implants fitted the patients' anatomy with
$0.6\pm0.2mm$ accuracy. The whole process from segmentation to implants' design
lasted about 5 minutes.
The proposed workflow allows for a fast and reliable personalisation of knee
implants, directly from the patient CT image without requiring any manual
intervention. It establishes a patient-specific pre-operative planning for TKA
in a very short time making it easily available for all patients. Combined with
efficient implant manufacturing techniques, this solution could help answer the
growing number of arthroplasties while reducing complications and improving the
patients' satisfaction.

通过使用人工神经网络，统计形状模型和多个形态参数，本研究提出了一种完全自动化的工作流，可以快速设计符合患者个体特征的关节置换植入物，从而在短时间内实现个性化的术前计划，改善关节置换手术的治疗效果。

用于病人特定骨科植入物设计的全自动工作流程：以全膝关节成形术为例

Fully automated workflow for the design of patient-specific orthopaedic  implants: application to total knee arthroplasty

Image segmentation in total knee arthroplasty is crucial for precise
preoperative planning and accurate implant positioning, leading to improved
surgical outcomes and patient satisfaction. The biggest challenges of image
segmentation in total knee arthroplasty include accurately delineating complex
anatomical structures, dealing with image artifacts and noise, and developing
robust algorithms that can handle anatomical variations and pathologies
commonly encountered in patients. The potential of using machine learning for
image segmentation in total knee arthroplasty lies in its ability to improve
segmentation accuracy, automate the process, and provide real-time assistance
to surgeons, leading to enhanced surgical planning, implant placement, and
patient outcomes. This paper proposes a methodology to use deep learning for
robust and real-time total knee arthroplasty image segmentation. The deep
learning model, trained on a large dataset, demonstrates outstanding
performance in accurately segmenting both the implanted femur and tibia,
achieving an impressive mean-Average-Precision (mAP) of 88.83 when compared to
the ground truth while also achieving a real-time segmented speed of 20 frames
per second (fps). We have introduced a novel methodology for segmenting
implanted knee fluoroscopic or x-ray images that showcases remarkable levels of
accuracy and speed, paving the way for various potential extended applications.

使用深度学习的新方法，能够实时准确地分割全膝关节置换手术图像，并取得了卓越的性能，为进一步的应用提供了可能。