Development and Validation of an Artificial Intelligence Preoperative Planning and Patient-Specific Instrumentation System for Total Knee Arthroplasty

Li, Songlin; Liu, Xingyu; Chen, Xi; Xu, Hongjun; Zhang, Yiling; Qian, Wenwei

doi:10.3390/bioengineering10121417

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…(c) Preoperative planning AI may contribute to improving the accuracy and, especially, the predictive ability of PP methods [87,88]. Machine learning algorithms can be used with CAD models of the different elements and steps needed to construct PP software (bone geometry, soft tissues, classification, material behaviors, collision detection, etc.).…”

Section: Role Of Artificial Intelligencementioning

confidence: 99%

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

Berhouet,

Samargandi

2024

Diagnostics

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In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These “markerless” systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

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Section: Role Of Artificial Intelligencementioning

confidence: 99%

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

Berhouet,

Samargandi

2024

Diagnostics

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Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods

Han,

Ma,

Lyu

et al. 2024

Robotics Computer Surgery

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BackgroundAutomatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings.MethodsHere, we present a new HTO landmark detection framework using an integration of optimised heatmap‐offset aggregation method and traditional feature extraction. Subjective and objective approaches were employed to reflect the final clinical acceptance of our model.ResultsAverage Mean Absolute Error of prediction results compared to the surgeon's gold standard was 0.35° for the hip‐knee‐ankle angle. The objective score rated by surgeons reached 4.4 on a scale of 5.ConclusionThe study demonstrated that the automatic detection method has great potential serving as an alternative to manual radiological analysis in practical surgical pre‐operative planning.

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Simulation of osteotomy in total knee arthroplasty with femoral extra-articular deformity assisted by artificial intelligence: a study based on three-dimensional models

Meng,

Yang,

Zhang

et al. 2024

J Orthop Surg Res

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Background The impact of extra-articular deformities (EADs) on lower limb alignment and collateral ligament integrity during total knee arthroplasty (TKA) poses significant challenges, increasing surgical complexity. Our study aims to evaluate the influence of EADs on mechanical axis alignment and the risk of collateral ligament injury during TKA using an AI-assisted surgical planning system, with the goal of minimizing ligament damage through precise and scientific planning. Methods A healthy volunteer underwent CT and MRI scans of the lower limbs. The scan images were imported into Mimics 20.0 software, and the reconstructed models were spatially aligned using 3-maticResearch 11.0 software. Using Unigraphics NX9.0 software, 50 three-dimensional models of femoral lateral joint deformities with varying positions and angles were created. Finally, TKA was simulated using the AI JOINT preoperative planning system. Results The larger the deformity angle and the closer it is to the knee joint, the more pronounced the deviation of the mechanical axis. During MA-aligned osteotomy, nine types of deformities can damage the collateral ligaments. After adjusting the varus/valgus of the prosthesis within a safe range of 3° and leaving a residual 3° varus/valgus in the lower limb alignment, only the 25° varus and 25° valgus deformities located at 90% of the femoral anatomical axis remain uncorrected. Conclusion For patients with osteoarthritis and concurrent EAD undergoing TKA, using reconstructed 3D models of the collateral ligaments for preoperative planning helps visually assess collateral ligament damage, providing a practical solution. Minimizing intra-articular osteotomies within a safe range and allowing some residual alignment deviation can reduce the risk of collateral ligament injury.

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Development and Validation of an Artificial Intelligence Preoperative Planning and Patient-Specific Instrumentation System for Total Knee Arthroplasty

Cited by 5 publications

References 46 publications

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods

Simulation of osteotomy in total knee arthroplasty with femoral extra-articular deformity assisted by artificial intelligence: a study based on three-dimensional models

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