Introduction. Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee. Methods. We developed subject-specific musculoskeletal models (MSM) based on magnetic resonance images (MRI) of four ex vivo left legs. The MSM allowed for the estimation of secondary knee kinematics (e.g. varus-valgus rotation) as a function of contact, ligament, and muscle forces in a native and post-TKA knee. We then used this model to train an ANN with 1800 simulations of knee flexion with random implant position variations in the ±3 mm and ±3° range from mechanical alignment. The trained ANN was used to find the implant alignment that resulted in the smallest mean-square-error (MSE) between native and post-TKA tibiofemoral kinematics, which we term the dynamic alignment. Results. Dynamic alignment average MSE kinematic differences to the native knees were 1.47 mm (± 0.89 mm) for translations and 2.89° (± 2.83°) for rotations. The implant variations required were in the range of ±3 mm and ±3° from the starting mechanical alignment. Discussion. In this study we showed that the developed tool has the potential to find an implant position that will restore native tibiofemoral kinematics in TKA. The proposed method might also be used with other alignment strategies, such as to optimize implant position towards native ligament strains. If native knee kinematics are restored, a more normal gait pattern can be achieved, which might result in improved patient satisfaction. The small changes required to achieve the dynamic alignment do not represent large modifications that might compromise implant survivorship. Conclusion. Patient-specific implant position predicted with MSM and ANN can restore native knee function in a post-TKA knee with a standard CR implant

Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic imaging that is limited to two- dimensions and is associated to low accuracies. We have previously introduced a full-dimensional position assessment framework based on registering intraoperative X-rays to preoperative volumetric images with sufficient accuracies. However, the framework requires a semi-manual process of pedicle screw segmentation and the intraoperative X-rays have to be taken from defined positions in space in order to avoid pedicle screws' head occlusion. This motivated us to develop advancements to the system to achieve higher levels of automation in the hope of higher clinical feasibility. In this study, we developed an automatic segmentation and X-ray adequacy assessment protocol. An artificial neural network was trained on a dataset that included a number of digitally reconstructed radiographs representing pedicle screw projections from different points of view. This model was able to segment the projection of any pedicle screw given an X-ray as its input with accuracy of 93% of the pixels. Once the pedicle screw was segmented, a number of descriptive geometric features were extracted from the isolated blob. These segmented images were manually labels as ‘adequate’ or ‘not adequate’ depending on the visibility of the screw axis. The extracted features along with their corresponding labels were used to train a decision tree model that could classify each X-ray based on its adequacy with accuracies on the order of 95%. In conclusion, we presented here a robust, fast and automated pedicle screw segmentation process, combined with an accurate and automatic algorithm for classifying views of pedicle screws as adequate or not. These tools represent a useful step towards full automation of our pedicle screw positioning assessment system

Aims

The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.