人工智能在髋关节置换术中的研究进展
Progress of Artificial Intelligence in Total Hip Arthroplasty
DOI: 10.12677/ACM.2024.142493, PDF,   
作者: 刘宇钊：山东大学附属省立医院关节外科，山东 济南；孙 水*：山东大学附属省立医院关节外科，山东 济南；山东第一医科大学附属省立医院关节外科，山东 济南
关键词: 人工智能；全髋关节置换术；机器学习；深度学习；综述；Artificial Intelligence； Total Hip Arthroplasty； Machine Learning； Deep Learning； Review
摘要: 全髋关节置换术(total hip arthroplasty, THA)可以通过减轻患者疼痛、恢复下肢功能以及矫正步态畸形，从而改善患者日常生活质量。随着医疗技术的不断进步，THA的数量也逐年增加。人工智能(artificial intelligence, AI)近年来在医疗领域应用十分广泛，本文通过对AI在THA中的研究进展进行总结，以期为患者和临床医生提供个性化治疗新思路。
Abstract: Total Hip Arthroplasty (THA) can improve the quality of daily life of patients by relieving pain, re-storing lower limb function, and correcting gait deformities. As medical technology continues to advance, the number of THA is increasing year by year. Artificial intelligence (AI) has been widely used in the medical field in recent years, and this paper summarizes the research progress of AI in THA in order to provide patients and clinicians with new ideas for personalized treatment.
文章引用：刘宇钊, 孙水. 人工智能在髋关节置换术中的研究进展[J]. 临床医学进展, 2024, 14(2): 3521-3527. https://doi.org/10.12677/ACM.2024.142493

参考文献

[1] Bini, S.A. (2018) Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? The Journal of Arthroplasty, 33, 2358-2361. [Google Scholar] [CrossRef] [PubMed]
[2] Anderson, D. (2019) Artificial Intelligence and Applications in PM&R. American Journal of Physical Medicine & Rehabilitation, 98, e128-e129. [Google Scholar] [CrossRef] 
[3] Do, S., Song, K.D. and Chung, J.W. (2020) Basics of Deep Learning: A Radiologist’s Guide to Understanding Published Radiology Articles on Deep Learning. Korean Journal of Radiology, 21, 33-41. [Google Scholar] [CrossRef] [PubMed]
[4] Laur, O. and Wang, B. (2022) Musculoskeletal Trauma and Artificial Intelligence: Current Trends and Projections. Skeletal Radiology, 51, 257-269. [Google Scholar] [CrossRef] [PubMed]
[5] Polkowski, G.G. and Clohisy, J.C. (2010) Hip Biomechanics. Sports Medicine and Arthroscopy Review, 18, 56-62. [Google Scholar] [CrossRef] 
[6] Dimon, JH. (1974) Surgical Anatomy of the Hip. Surgical Clinics of North America, 54, 1327-1335. [Google Scholar] [CrossRef] 
[7] Varacallo, M., Luo, T.D. and Johanson, N.A. (2022) Total Hip Arthroplasty Techniques. StatPearls Publishing, Treasure Island, FL.
[8] Singh, J.A., Yu, S., Chen, L. and Cleveland, J.D. (2019) Rates of Total Joint Replacement in the United States: Future Projections to 2020-2040 Using the National Inpatient Sample. The Journal of Rheumatology, 46, 1134-1140. [Google Scholar] [CrossRef] [PubMed]
[9] Chang, C.Y. and Huang, A.J. (2013) MR Imaging of Normal Hip Anatomy. Magnetic Resonance Imaging Clinics of North America, 21, 1-19. [Google Scholar] [CrossRef] [PubMed]
[10] Shon, W.Y., Sharma, V., Keon, O.J., Moon, J.G. and Suh, D.H. (2014) Can Pelvic Tilting Be Ignored in Total Hip Arthroplasty? International Journal of Surgery Case Reports, 5, 633-636. [Google Scholar] [CrossRef] [PubMed]
[11] Bhaskar, D., Rajpura, A. and Board, T. (2017) Current Concepts in Acetabular Positioning in Total Hip Arthroplasty. Indian Journal of Orthopaedics, 51, 386-396. [Google Scholar] [CrossRef] 
[12] Durand-Hill, M., Henckel, J., Satchithananda, K., et al. (2016) Calculating the Hip Center of Rotation Using Contralateral Pelvic Anatomy. Journal of Orthopaedic Research, 34, 1077-1083. [Google Scholar] [CrossRef] [PubMed]
[13] Shun, M., Wang, Z.J. and Rui, L. (2016) A CNN Regression Approach for Real-Time 2D/3D Registration. IEEE Transactions on Medical Imaging, 35, 1352-1363. [Google Scholar] [CrossRef] 
[14] Jodeiri, A., Zoroofi, R.A., Hiasa, Y., et al. (2020) Fully Automatic Estimation of Pelvic Sagittal Inclination from Anterior-Posterior Radiography Image Using Deep Learning Framework. Computer Methods and Programs in Biomedicine, 184, Article 105282. [Google Scholar] [CrossRef] [PubMed]
[15] Jang, S.J., Kunze, K.N., Vigdorchik, J.M., Jerabek, S.A., Mayman, D.J. and Sculco, P.K. (2022) John Charnley Award: Deep Learning Prediction of Hip Joint Center on Standard Pelvis Radiographs. The Journal of Arthroplasty, 37, S400-S407.e1. [Google Scholar] [CrossRef] [PubMed]
[16] Thirion, T., Georis, P. and Gillet, P. (2019) [Preoperative Planning Interest of a Total Hip Prosthesis]. Revue Médicale de Liège, 74, 593-597.
[17] Chen, X., Liu, X., Wang, Y., et al. (2022) Development and Validation of an Artificial Intelligence Preoperative Planning System for Total Hip Arthroplasty. Frontiers in Medicine, 9, Article 841202. [Google Scholar] [CrossRef] [PubMed]
[18] Huo, J., Huang, G., Han, D., et al. (2021) Value of 3D Preopera-tive Planning for Primary Total Hip Arthroplasty Based on Artificial Intelligence Technology. Journal of Orthopaedic Surgery and Research, 16, Article No. 156. [Google Scholar] [CrossRef] [PubMed]
[19] Ding, X., Zhang, B., Li, W., et al. (2021) Value of Preoperative Three-Dimensional Planning Software (AI-HIP) in Primary Total Hip Arthroplasty: A Retrospective Study. The Journal of International Medical Research, 49. [Google Scholar] [CrossRef] [PubMed]
[20] Mouille, B., Higuera, C., Woicehovich, L., et al. (2016) How to Succeed in Bundled Payments for Total Joint Replacement. Catalyst Carryover, 2, No. 5.
[21] Ramkumar, P.N., Navarro, S.M., Haeberle, H.S., et al. (2019) Development and Validation of a Machine Learning Algorithm after Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models. The Journal of Arthroplasty, 34, 632-637. [Google Scholar] [CrossRef] [PubMed]
[22] Lazic, I., Hinterwimmer, F., Langer, S., et al. (2022) Prediction of Complications and Surgery Duration in Primary Total Hip Arthroplasty Using Machine Learning: The Necessity of Modified Algorithms and Specific Data. Journal of Clinical Medicine, 11, Article 2147. [Google Scholar] [CrossRef] [PubMed]
[23] Radl, R., Aigner, C., Hungerford, M., Pascher, A. and Windhager, R. (2000) Proximal Femoral Bone Loss and Increased Rate of Fracture with a Proximally Hydroxyapatite-Coated Femoral Component. The Journal of Bone and Joint Surgery, 82, 1151-1155. [Google Scholar] [CrossRef] 
[24] Gargiulo, P., Edmunds, K.J., Gíslason, M.K., et al. (2018) Patient-Specific Mobility Assessment to Monitor Recovery after Total Hip Arthroplasty. Proceedings of the Institution of Mechanical Engineers Part H, Journal of Engineering in Medicine, 232, 1048-1059. [Google Scholar] [CrossRef] [PubMed]
[25] Ricciardi, C., Jónsson, H., Jacob, D., et al. (2020) Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty. Diagnostics (Basel, Switzerland), 10, Article 815. [Google Scholar] [CrossRef] [PubMed]
[26] Siljander, M.P., McQuivey, K.S., Fahs, A.M., Galasso, L.A., Serdahely, K.J. and Karadsheh, M.S. (2018) Current Trends in Patient-Reported Outcome Measures in Total Joint Arthroplasty: A Study of 4 Major Orthopaedic Journals. The Journal of Arthroplasty, 33, 3416-3421. [Google Scholar] [CrossRef] [PubMed]
[27] Halawi, M.J., Jongbloed, W., Baron, S., Savoy, L., Cote, M.P. and Lieberman, J.R. (2020) Patient-Reported Outcome Measures Are Not a Valid Proxy for Patient Satisfaction in Total Joint Arthroplasty. The Journal of Arthroplasty, 35, 335-339. [Google Scholar] [CrossRef] [PubMed]
[28] Mancuso, C.A., Salvati, E.A., Johanson, N.A., Peterson, M.G. and Charlson, M.E. (1997) Patients’ Expectations and Satisfaction with Total Hip Arthroplasty. The Journal of Arthroplasty, 12, 387-396. [Google Scholar] [CrossRef] 
[29] Anakwe, R.E., Jenkins, P.J. and Moran, M. (2011) Predict-ing Dissatisfaction after Total Hip Arthroplasty: A Study of 850 Patients. The Journal of Arthroplasty, 26, 209-213. [Google Scholar] [CrossRef] [PubMed]
[30] Palazzo, C., Jourdan, C., Descamps, S., et al. (2014) Determinants of Satisfaction 1 Year after Total Hip Arthroplasty: The Role of Expectations Fulfilment. BMC Musculoskeletal Dis-orders, 15, Article No. 53. [Google Scholar] [CrossRef] [PubMed]
[31] Fontana, M.A., Lyman, S., Sarker, G.K., Padgett, D.E. and Mac-Lean, C.H. (2019) Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Im-portant Differences from Total Joint Arthroplasty? Clinical Orthopaedics and Related Research, 477, 1267-1279. [Google Scholar] [CrossRef] 
[32] Huber, M., Kurz, C. and Leidl, R. (2019) Predicting Pa-tient-Reported Outcomes Following Hip and Knee Replacement Surgery Using Supervised Machine Learning. BMC Medical Informatics and Decision Making, 19, Article No. 3. [Google Scholar] [CrossRef] [PubMed]
[33] Zhang, S., Chen, J.Y., Pang, H.N., Lo, N.N., Yeo, S.J. and Liow, M.H.L. (2021) Development and Internal Validation of Machine Learning Algorithms to Predict Patient Satisfaction after Total Hip Arthroplasty. Arthroplasty (London, England), 3, Article No. 33. [Google Scholar] [CrossRef] [PubMed]
[34] Fillingham, Y.A., Ramkumar, D.B., Jevsevar, D.S., et al. (2018) The Safety of Tranexamic Acid in Total Joint Arthroplasty: A Direct Meta-Analysis. The Journal of Arthroplasty, 33, 3070-3082.e1. [Google Scholar] [CrossRef] [PubMed]
[35] Bedard, N.A., Pugely, A.J., Lux, N.R., Liu, S.S., Gao, Y. and Callaghan, J.J. (2017) Recent Trends in Blood Utilization after Primary Hip and Knee Arthroplasty. The Journal of Arthroplasty, 32, 724-727. [Google Scholar] [CrossRef] [PubMed]
[36] Cohen-Levy, W.B., Klemt, C., Tirumala, V., et al. (2023) Artificial Neural Networks for the Prediction of Transfusion Rates in Primary Total Hip Arthroplasty. Archives of Orthopaedic and Trauma Surgery, 143, 1643-1650. [Google Scholar] [CrossRef] [PubMed]
[37] Goesling, J., Moser, S.E., Zaidi, B., et al. (2016) Trends and Predictors of Opioid Use after Total Knee and Total Hip Arthroplasty. Pain, 157, 1259-1265. [Google Scholar] [CrossRef] [PubMed]
[38] Inacio, M.C.S., Hansen, C., Pratt, N.L., Graves, S.E. and Roughead, E.E. (2016) Risk Factors for Persistent and New Chronic Opioid Use in Patients Undergoing Total Hip Arthroplasty: A Retrospective Cohort Study. BMJ Open, 6, e010664. [Google Scholar] [CrossRef] [PubMed]
[39] Bedard, N.A., Pugely, A.J., Dowdle, S.B., Duchman, K.R., Glass, N.A. and Callaghan, J.J. (2017) Opioid Use Following Total Hip Arthroplasty: Trends and Risk Factors for Prolonged Use. The Journal of Arthroplasty, 32, 3675-3679. [Google Scholar] [CrossRef] [PubMed]
[40] Bedard, N.A., Pugely, A.J., Westermann, R.W., Duchman, K.R., Glass, N.A. and Callaghan, J.J. (2017) Opioid Use after Total Knee Arthroplasty: Trends and Risk Factors for Prolonged Use. The Journal of Arthroplasty, 32, 2390-2394. [Google Scholar] [CrossRef] [PubMed]
[41] Swami, A. and Jain, R. (2013) Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
[42] Gabriel, R.A., Harjai, B., Prasad, R.S., et al. (2022) Machine Learning Approach to Predicting Persistent Opioid Use Following Lower Extremity Joint Arthroplasty. Regional Anesthesia and Pain Medicine, 47, 313-319. [Google Scholar] [CrossRef] [PubMed]
[43] Wilson, N.A., Jehn, M., York, S. and Davis, C.M. (2014) Revi-sion Total Hip and Knee Arthroplasty Implant Identification: Implications for Use of Unique Device Identification 2012 AAHKS Member Survey Results. The Journal of Arthroplasty, 29, 251-255. [Google Scholar] [CrossRef] [PubMed]
[44] Gong, Z., Fu, Y., He, M. and Fu, X. (2022) Automated Identifica-tion of Hip Arthroplasty Implants Using Artificial Intelligence. Scientific Reports, 12, Article No. 12179. [Google Scholar] [CrossRef] [PubMed]
[45] Karnuta, J.M., Murphy, M.P., Luu, B.C., et al. (2023) Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs. The Journal of Arthroplasty, 38, 1998-2003.e1. [Google Scholar] [CrossRef] [PubMed]
[46] Bozic, K.J., Kurtz, S.M., Lau, E., Ong, K., Vail, T.P. and Berry, D.J. (2009) The Epidemiology of Revision Total Hip Arthroplasty in the United States. The Journal of Bone and Joint Surgery, 91, 128-133. [Google Scholar] [CrossRef] 
[47] Kurtz, S., Ong, K., Lau, E., Mowat, F. and Halpern, M. (2007) Pro-jections of Primary and Revision Hip and Knee Arthroplasty in the United States from 2005 to 2030. The Journal of Bone and Joint Surgery, 89, 780-785. [Google Scholar] [CrossRef] 
[48] Trampuz, A. and Zimmerli, W. (2008) Diagnosis and Treatment of Implant-Associated Septic Arthritis and Osteomyelitis. Current Infectious Disease Reports, 10, 394-403. [Google Scholar] [CrossRef] [PubMed]
[49] Parvizi, J., Ghanem, E., Sharkey, P., Aggarwal, A., Burnett, R.S.J. and Barrack, R.L. (2008) Diagnosis of Infected Total Knee: Findings of a Multicenter Database. Clinical Orthopaedics and Related Research, 466, 2628-2633. [Google Scholar] [CrossRef] [PubMed]
[50] Zoccali, C., Teori, G. and Salducca, N. (2009) The Role of FDG-PET in Distinguishing between Septic and Aseptic Loosening in Hip Prosthesis: A Review of Literature. Interna-tional Orthopaedics, 33, 1-5. [Google Scholar] [CrossRef] [PubMed]
[51] Signore, A., Sconfienza, L.M., Borens, O., et al. (2019) Con-sensus Document for the Diagnosis of Prosthetic Joint Infections: A Joint Paper by the EANM, EBJIS, and ESR (with ESCMID Endorsement). European Journal of Nuclear Medicine and Molecular Imaging, 46, 971-988. [Google Scholar] [CrossRef] [PubMed]
[52] Loppini, M., Gambaro, F.M., Chiappetta, K., Grappiolo, G., Bianchi, A.M. and Corino, V.D.A. (2022) Automatic Identification of Failure in Hip Replacement: An Artificial Intel-ligence Approach. Bioengineering (Basel, Switzerland), 9, Article 288. [Google Scholar] [CrossRef] [PubMed]
[53] Shah, R.F., Bini, S.A., Martinez, A.M., Pedoia, V. and Vail, T.P. (2020) Incremental Inputs Improve the Automated Detection of Implant Loosening Using Machine-Learning Al-gorithms. The Bone & Joint Journal, 102-B, 101-106. [Google Scholar] [CrossRef] 
[54] Salassa, T., Hoeffel, D., Mehle, S., Tatman, P. and Gioe, T.J. (2014) Efficacy of Revision Surgery for the Dislocating Total Hip Arthroplasty: Report from a Large Community Registry. Clinical Orthopaedics and Related Research, 472, 962-967. [Google Scholar] [CrossRef] [PubMed]
[55] Borjali, A., Magnéli, M., Shin, D., Malchau, H., Muratoglu, O.K. and Varadarajan, K.M. (2021) Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation. Computers in Biol-ogy and Medicine, 129, Article 104140. [Google Scholar] [CrossRef] [PubMed]
[56] Rouzrokh, P., Ramazanian, T., Wyles, C.C., et al. (2021) Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty from Postoperative Radiographs. The Journal of Arthroplasty, 36, 2197-2203.e3. [Google Scholar] [CrossRef] [PubMed]

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