Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.
Int J Med Robot
; 20(4): e2664, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38994900
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.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
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Arthroplasty, Replacement, Knee
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Robotic Surgical Procedures
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Deep Learning
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Knee Joint
Limits:
Aged
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Female
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Humans
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Male
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Middle aged
Language:
En
Journal:
Int J Med Robot
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: