| [1] |
Van Melle W, Shortliffe EH, Buchanan BG. EMYCIN: a knowledge engineer’s tool for constructing rule-based expert systems//Shortliffe EH, Buchanan BG. Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project. Reading: Addison-Wesley Publishing Company, 1984: 302－313.
|
| [2] |
田启川，王满丽. 深度学习算法研究进展[J]. 计算机工程与应用，2019，55(22)：25－33.
|
| [3] |
Xue Y, Zhang R, Deng Y, et al. A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis[J/OL]. PloS one, 2017, 12(6): e0178992. DOI: 10.1371/journal.pone.0178992
|
| [4] |
Sato Y, Takegami Y, Asamoto T, et al. Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study[J/OL]. BMC Musculoskelet Disord, 2021, 22(1): 407. DOI: 10.1186/s12891-021-04260-2.
|
| [5] |
Klontzas ME, Manikis GC, Nikiforaki K, et al. Radiomics and machine learning can differentiate transient osteoporosis from avascular necrosis of the hip[J/OL]. Diagnostics (Basel), 2021, 11(9): 1686.DOI: 10.3390/diagnostics11091686.
|
| [6] |
Li Y, Li Y, Tian H. Deep learning-based end-to-end diagnosis system for avascular necrosis of femoral head[J]. IEEE J Biomed Health Inform, 2020, 25(6): 2093－2102.
|
| [7] |
Tiulpin A, Thevenot J, Rahtu E, et al. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep Learning-Based approach[J/OL]. Sci Rep, 2018, 8(1): 1727. DOI: 10.1038/s41598-018-20132-7.
|
| [8] |
Norman B, Pedoia V, Noworolski A, et al. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs[J]. J Digit Imaging, 2019, 32(3): 471－477.
|
| [9] |
Leung K, Zhang B, Tan J, et al. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs：data from the osteoarthritis initiative[J]. Radiology, 2020, 296(3): 584－593.
|
| [10] |
Kundu S, Ashinsky BG, Bouhrara M, et al. Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning[J]. Proc Natl Acad Sci USA, 2020, 117(40): 24709－24719.
|
| [11] |
Pierson E, Cutler DM, Leskovec J, et al. An algorithmic approach to reducing unexplained pain disparities in underserved populations[J]. Nat Med, 2021, 27(1): 136－140.
|
| [12] |
吴东，柴伟，刘星宇，等．人工智能全髋关节置换术髋臼杯放置算法的实验研究[J]．中华骨科杂志，2021，41(3)：176－185
|
| [13] |
吴东，刘星宇，张逸凌，等．人工智能辅助全髋关节置换术三维规划系统的研发及临床应用研究[J]．中国修复重建外科杂志，2020，34(9)：1077－1084.
|
| [14] |
Huo J, Huang G, Han D, et al. Value of 3D preoperative planning for primary total hip arthroplasty based on artificial intelligence technology[J/OL]. J Orthop Surg Res, 2021, 16(1): 156.DOI: 10.1186/s13018-021-02294-9.
|
| [15] |
夏天卫，刘星宇，刘金柱，等．人工智能术前规划系统辅助人工全髋关节置换术治疗成人Crowe Ⅳ型先天性髋关节发育不良的疗效研究[J]．中国修复重建外科杂志，2021，35(10)：1265－1272.
|
| [16] |
Lustig S, Sappey-Marinier E, Fary C, et al. Personalized alignment in total knee arthroplasty: current concepts[J/OL]. SICOT-J, 2021, 7：19. DOI: 10.1051/sicotj/2021021.
|
| [17] |
宋平，吴东，刘星宇，等．一例人工智能三维规划系统辅助全膝关节置换术[J]．骨科，2021，12(3)：281－283.
|
| [18] |
北京长木谷医疗科技有限公司．基于深度学习的单髁置换术前规划方法和相关设备：中国，CN202110185454.0[P].2021－06－15.
|
| [19] |
Shah AA, Devana SK, Lee C, et al. Development of a novel，potentially Universal machine learning algorithm for prediction of complications after total hip arthroplasty[J]. J Arthroplasty, 2021, 36(5): 1655－1662. e1.
|
| [20] |
Jo C，Ko S，Shin WC, et al. Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm[J]. Knee Surg Sports Traumatol Arthrosc, 2020, 28(6): 1757－1764.
|
| [21] |
Cohen-Levy WB, Klemt C, Tirumala V, et al. Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty[J/OL]. Arch Orthop Trauma Surg, 2022. DOI: 10.1007/s00402-022-04391-8.
|
| [22] |
Panesar S, Cagle Y, Chander D, et al. Artificial intelligence and the future of surgical robotics[J]. Ann Surg, 2019, 270(2): 223－226.
|
| [23] |
Kamara E, Robinson J, Bas MA, et al. Adoption of robotic vs fluoroscopic guidance in total hip arthroplasty：is acetabular positioning improved in the learning curve？[J]. J Arthroplasty, 2017, 32(1): 125－130.
|
| [24] |
王波，周楚乔，彭正午，等．机器人辅助与传统手工全髋置换术疗效的Meta分析[J/CD]．中华关节外科杂志(电子版)，2021，15(5)：554－561.
|
| [25] |
Jacofsky DJ, Allen M. Robotics in arthroplasty: a comprehensive review[J]. J Arthroplasty, 2016, 31(10): 2353－2363.
|
| [26] |
Leelasestaporn C, Tarnpichprasert T, Arirachakaran A, et al. Comparison of 1-year outcomes between MAKO versus NAVIO robot-assisted medial UKA: nonrandomized, prospective, comparative study[J/OL]. Knee Surg Relat Res, 2020, 32(1): 13. DOI: 10.1186/s43019-020-00030-x.
|
| [27] |
Porcelli P, Marmotti A, Bellato E, et al. Comparing different approaches in robotic－assisted surgery for unicompartmental knee arthroplasty：outcomes at a short-term follow-up of MAKO versus NAVIO system[J]. J Biol Regul Homeost Agents, 2020, 34(4 Suppl 3): 393－404.
|
| [28] |
Batailler C, Hannouche D, Benazzo F, et al. Concepts and techniques of a new robotically assisted technique for total knee arthroplasty: the ROSA knee system[J]. Arch Orthop Trauma Surg, 2021, 141(12): 2049－2058.
|
| [29] |
Liow MHL, Chin PL, Pang HN, et al. THINK surgical TSolution-One? (Robodoc) total knee arthroplasty[J/OL]. SICOT-J, 2017, 3: 63. DOI: 10.1051/sicotj/2017052.
|
| [30] |
Kayani B, Konan S, Tahmassebi J, et al. Robotic-arm assisted total knee arthroplasty is associated with improved early functional recovery and reduced time to hospital discharge compared with conventional jig-based total knee arthroplasty：a prospective cohort study[J]. Bone Joint J, 2018, 100－B(7): 930－937.
|
| [31] |
Kayani B, Konan S, Ayuob A, et al. Robotic technology in total knee arthroplasty：a systematic review[J]. EFORT Open Rev, 2019, 4(10): 611－617.
|
| [32] |
Chin BZ, Tan SH, Chua KX, et al. Robot-assisted versus conventional total and unicompartmental knee arthroplasty：a meta-analysis of radiological and functional outcomes[J]. J Knee Surg, 2021, 34(10): 1064－1075.
|
| [33] |
Khlopas A, Chughtai M, Hampp EL, et al. Robotic-arm assisted total knee arthroplasty demonstrated soft rissue protection[J]. Surg Technol Int, 2017, 30: 441－446.
|
| [34] |
Kim YH, Yoon SH, Park JW. Does robotic-assisted TKA result in better outcome scores or long-term survivorship than conventional TKA？A randomized, controlled trial[J]. Clin Orthop Relat Res, 2020, 478(2): 266－275.
|
| [35] |
王俏杰，柴伟，王琦，等．机器人辅助下膝关节单髁置换术初步临床结果[J]．中华解剖与临床杂志，2017，22(2)：108－115.
|
| [36] |
北京市医疗保障局，北京市卫生健康委员会，北京市人力资源和社会保障局. 关于规范调整物理治疗类等医疗服务价格项目的通知[EB/OL]．(2021－08－25)．[2021－10－23]．
URL
|
| [37] |
Polus JS, Bloomfield RA, Vasarhelyi EM, et al. Machine learning predicts the fall risk of total hip arthroplasty patients based on wearable sensor instrumented performance tests[J]. J Arthroplasty, 2021, 36(2): 573－578.
|
| [38] |
Bini SA, Shah RF, Bendich I, et al. Machine learning algorithms can use wearable sensor data to accurately predict six-week patient-reported outcome scores following joint replacement in a prospective trial[J]. J Arthroplasty, 2019, 34(10): 2242－2247.
|
| [39] |
Rouzrokh P, Ramazanian T, Wyles CC, et al. Deep learning artificial intelligence model for assessment of hip dislocation risk following primary total hip arthroplasty from postoperative radiographs[J]. J Arthroplasty, 2021, 36(6): 2197－2203. e3.
|
| [40] |
Rouzrokh P, Wyles CC, Philbrick KA, et al. A deep learning tool for automated radiographic measurement of acetabular component inclination and version after total hip arthroplasty[J]. J Arthroplasty, 2021, 36(7): 2510－2517.e6.
|
| [41] |
Shah RF, Bini SA, Martinez AM, et al. Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms[J]. Bone Joint J, 2020, 102-B(6 Supple A): 101－106.
|
| [42] |
孔祥朋，付君，陈继营，等.5G通信技术远程指导机器人辅助全髋关节置换术两例[J]．中国修复重建外科杂志，2020，34(11)：1492－1493.
|