AI Article Synopsis
- The study introduced a new deep learning model called Dual-path Double Attention Transformer (DDA-Transformer) for quick and accurate segmentation of knee joint CT images, specifically targeting improvements for robotic-assisted total knee arthroplasty (TKA).
- The DDA-Transformer's performance was evaluated against six other models, showing superior metrics in segmentation accuracy and speed, outperforming nnUnet, TransUnet, and 3D-Unet significantly.
- The findings indicated that the DDA-Transformer not only enhances CT image segmentation for knee joints but also improves the surgical precision of robotic-assisted TKA procedures.
Article Abstract
Background: This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA).
Methods: The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated.
Results: Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy.
Conclusions: DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.
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| http://dx.doi.org/10.1002/rcs.2664 | DOI Listing |
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