Study on artificial intelligence-based algorithm for acetabular cup in total hip arthroplasty
10.3760/cma.j.cn121113-20201110-00653
- VernacularTitle:人工智能全髋关节置换术髋臼杯放置算法的实验研究
- Author:
Dong WU
;
Wei CHAI
;
Xingyu LIU
;
Yicheng AN
;
Yiling ZHANG
;
Jiying CHEN
;
Peifu TANG
- From:
Chinese Journal of Orthopaedics
2021;41(3):176-185
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a set of algorithms that could predict the precise size of acetabular cup preoperatively by the deep learning neural network technology.Methods:Retrospective analysis was performed on 30 patients with femoral head necrosis from April 2019 to April 2020, including 15 males and 15 females. At the age of (54.8±10.5) years (range 33-72 years). Thirteen hips on the left and seventeen hips on the right, who underwent primary unilateral THA. Based on the manually segmented hip joint CT database, a deep learning convolutional neural network was trained to realize automatic segmentation. A customized algorithm was created to fit the surface of the acetabulum. By the application of another deep learning convolutional neural network, the identification of anatomical points of the pelvis and correction of the pelvic position were realized. So that the placement of the acetabulum cup could be done. DOC (dice overlap coefficients) as well as the average error parameter were adopted to evaluate the accuracy of the above steps. The novel algorithm and Orthoview software were retrospectively used to template the acetabular cup separately. The results of both groups were compared with the actual size and the coincidence rate was calculated to evaluate the accuracy of the novel algorithm. To verify this algorithm, the conformance rate was calculated respectively.Results:Compared with other classical segmentation networks, the G-NET network can segment the pelvic with femoral head necrosis more accurately (DOC 92.51%± 6.70%). It also has better robustness. The average error of the point recognition network is 0.87 pixels. Among the 30 patients, the AI-based algorithm group had a complete coincidence rate of 96.7% and the Orthoview group had a complete coincidence rate of 73.3%. The difference was statistically significant ( χ2=6.405, P=0.011). Conclusion:The artificial intelligence-based algorithm can segment the CT image series and identify the feature points of the patient's hip accurately. Compared with the conventional 2D preoperative planning method, the AI-based algorithm is relatively more accurate. This artificial intelligence-based 3D preoperative software has promising prospect to makeaccurate surgical plan efficiently.
