{Reference Type}: Journal Article
{Title}: Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.
{Author}: Liu X;Li S;Zou X;Chen X;Xu H;Yu Y;Gu Z;Liu D;Li R;Wu Y;Wang G;Liao H;Qian W;Zhang Y;
{Journal}: Int J Med Robot
{Volume}: 20
{Issue}: 4
{Year}: 2024 Aug
{Factor}: 2.483
{DOI}: 10.1002/rcs.2664
{Abstract}: <B>BACKGROUND: </B>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).<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.