摘要
目的研发基于人工智能深度学习技术的全髋关节置换术(total hip arthroplasty,THA)髋臼假体型号算法并进行初步验证。方法回顾性分析2019年4月至2020年4月30例股骨头坏死患者资料,其中男15例,女15例;年龄(54.8±10.5)岁(范围33~72岁),左侧13髋,右侧17髋,均接受初次单侧THA。在完成髋关节图像手工标注的基础上,训练人工智能深度学习卷积神经网络对患者髋关节CT骨质进行分割,而后识别骨盆解剖标志位点,并对骨盆位置进行矫正并模拟安放髋臼杯,分别采用dice overlap coefficients(DOC)、平均误差等参数对上述步骤的精度进行评估,最终形成人工智能髋臼假体型号算法。并使用该算法与Orthoview二维术前规划软件分别对患者髋臼杯大小进行规划,将两组规划结果与已完成的实际手术结果进行比对,分别计算其符合率,从而回顾性验证本算法的规划效果。结果在算法方面,与其他经典分割网络相比,G-net网络可更精准的完成对股骨头坏死髋关节骨质的分割,DOC为92.51%±6.70%,且具有更好的鲁棒性(robustness),点识别网络平均误差为0.87个像素值。在临床应用效果方面,人工智能组完全符合率为96.7%(29/30),较Orthoview组的73.3%(22/30)高23.4%,差异有统计学意义(χ^2=6.405,P=0.011)。结论深度学习技术可精准分割患者髋关节CT图像,识别髋关节特征点,人工智能THA髋臼杯放置算法与传统二维术前规划方式相比具有较高的准确性。此算法有望实现准确、快速的THA三维术前规划。
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(χ^2=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.
作者
吴东
柴伟
刘星宇
安奕成
张逸凌
陈继营
唐佩福
Wu Dong;Chai Wei;Liu Xingyu;An Yicheng;Zhang Yiling;Chen Jiying;Tang Peifu(Department of Orthopedics,the first Medical Centre,Chinese PLA General Hospital,Beijing 610041,China;School of Life Sciences,Tsinghua University,Beijing 100853,China;Stanford University of Computer Science,Stanford 94305,USA;Harvard Medical School,Boston 02138,USA)
出处
《中华骨科杂志》
CAS
CSCD
北大核心
2021年第3期176-185,共10页
Chinese Journal of Orthopaedics
基金
国家自然科学基金项目(81772320)
中国人民解放军总医院2019年度医疗大数据与人工智能研发项目(2019MBD-041)。
关键词
人工智能
学习
神经网络(计算机)
关节成形术
置换
髋
规划制订
Artificial intelligence
Learning
Neural networks(computer)
Arthroplasty,replacement,hip
Program Development
