Open Access
Review
Issue |
SICOT-J
Volume 10, 2024
|
|
---|---|---|
Article Number | 49 | |
Number of page(s) | 8 | |
Section | Knee | |
DOI | https://doi.org/10.1051/sicotj/2024044 | |
Published online | 21 November 2024 |
- Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C (2020) Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am 102, 830–840. [CrossRef] [PubMed] [Google Scholar]
- Farhadi F, Barnes MR, Sugito HR, Sin JM, Henderson ER, Levy JJ (2022) Applications of artificial intelligence in orthopaedic surgery. Front Med Technol 4, 995526. [CrossRef] [PubMed] [Google Scholar]
- Cieza A, Causey K, Kamenov K, Hanson SW, Chatterji S, Vos T (2021) Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 2006–2017. [Google Scholar]
- Farrow L, Ashcroft GP, Zhong M, Anderson L (2022) Using artificial intelligence to revolutionise the patient care pathway in hip and knee arthroplasty (ARCHERY): protocol for the development of a clinical prediction model. JMIR Res Protoc 11, e37092. [CrossRef] [PubMed] [Google Scholar]
- Yapp LZ, Clarke JV, Moran M, Simpson A, Scott CEH (2021) National operating volume for primary hip and knee arthroplasty in the COVID-19 era: a study utilizing the Scottish arthroplasty project dataset. Bone Jt Open 2, 203–210. [CrossRef] [PubMed] [Google Scholar]
- Nikolova S, Harrison M, Sutton M (2016) The impact of waiting time on health gains from surgery: evidence from a National Patient-reported outcome dataset. Health Econ 25, 955–968. [CrossRef] [PubMed] [Google Scholar]
- He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K (2019) The practical implementation of artificial intelligence technologies in medicine. Nat Med 25, 30–36. [CrossRef] [PubMed] [Google Scholar]
- Cabitza F, Locoro A, Banfi G (2018) Machine Learning in Orthopedics: A Literature Review. Front Bioeng Biotechnol 6, 75. [CrossRef] [PubMed] [Google Scholar]
- Xue Y, Zhang R, Deng Y, Chen K, Jiang T (2017) A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis. PLoS One 12, e0178992. [CrossRef] [PubMed] [Google Scholar]
- Üreten K, Arslan T, Gültekin KE, Demir AND, Özer HF, Bilgili Y (2020) Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods. Skeletal Radiol 49, 1369–1374. [CrossRef] [PubMed] [Google Scholar]
- Swiecicki A, Li N, O’Donnell J, Said N, Yang J, Mather RC, Jiranek WA, Mazurowski MA (2021) Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Comput Biol Med 133, 104334. [CrossRef] [PubMed] [Google Scholar]
- Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S (2018) Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Scientific Rep 8, 1727. [CrossRef] [Google Scholar]
- Lee LS, Chan PK, Wen C, Fung WC, Cheung A, Chan VWK, Cheung MH, Fu H, Yan CH, Chiu KY (2022) Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review. Arthroplasty 4, 16. [CrossRef] [PubMed] [Google Scholar]
- Sharma S (2023) Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential. SICOT J 9, 21. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- Guan B, Liu F, Haj-Mirzaian A, Demehri S, Samsonov A, Neogi T, Guermazi A, Kijowski R (2020) Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-month follow-up period. Osteoarthritis Cartilage 28, 428–437. [CrossRef] [PubMed] [Google Scholar]
- Arbeeva L, Minnig MC, Yates KA, Nelson AE (2023) Machine learning approaches to the prediction of osteoarthritis phenotypes and outcomes. Curr Rheumatol Rep 25, 213–225. [CrossRef] [PubMed] [Google Scholar]
- Schiratti JB, Dubois R, Herent P, Cahane D, Dachary J, Clozel T, Wainrib G, Keime-Guibert F, Lalande A, Pueyo M, Guillier R, Gabarroca C, Moingeon P (2021) A deep learning method for predicting knee osteoarthritis radiographic progression from MRI. Arthritis Res Ther 23, 262. [CrossRef] [PubMed] [Google Scholar]
- Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, Cho K, Chang G, Deniz CM (2020) Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative. Radiology 296, 584–593. [CrossRef] [PubMed] [Google Scholar]
- Heisinger S, Hitzl W, Hobusch GM, Windhager R, Cotofana S (2020) Predicting total knee replacement from symptomology and radiographic structural change using artificial neural networks-data from the Osteoarthritis Initiative (OAI). J Clin Med 9, 1298. [CrossRef] [PubMed] [Google Scholar]
- Özden F, Nadiye Karaman Ö, Tuğay N, Yalın Kilinç C, Mihriban Kilinç R, Umut Tuğay B (2020) The relationship of radiographic findings with pain, function, and quality of life in patients with knee osteoarthritis. J Clin Orthop Trauma 11, S512–S517. [CrossRef] [PubMed] [Google Scholar]
- Bedson J, Croft PR (2008) The discordance between clinical and radiographic knee osteoarthritis: a systematic search and summary of the literature. BMC Musculoskelet Disord 9, 116. [CrossRef] [PubMed] [Google Scholar]
- Lee HS, Hwang S, Kim SH, Joon NB, Kim H, Hong YS, Kim S (2024) Automated analysis of knee joint alignment using detailed angular values in long leg radiographs based on deep learning. Sci Rep 14, 7226. [CrossRef] [PubMed] [Google Scholar]
- Vidhani FR, Woo JJ, Zhang YB, Olsen RJ, Ramkumar PN (2024) Automating linear and angular measurements for the hip and knee after computed tomography: validation of a three-stage deep learning and computer vision-based pipeline for pathoanatomic assessment. Arthroplasty Today 27, 101394. [CrossRef] [PubMed] [Google Scholar]
- Schwartz AM, Farley KX, Guild GN, Bradbury TL (2020) Projections and epidemiology of revision hip and knee arthroplasty in the United States to 2030. J Arthroplasty 35, S79–S85. [CrossRef] [PubMed] [Google Scholar]
- Rouzrokh P, Mickley JP, Khosravi B, Faghani S, Moassefi M, Schulz WR, Erickson BJ, Taunton MJ, Wyles CC (2023) THA-AID: Deep learning tool for total hip arthroplasty automatic implant detection with uncertainty and outlier quantification. J Arthroplasty 39, 966–973. [Google Scholar]
- Thirukumaran CP, Zaman A, Rubery PT, Calabria C, Li Y, Ricciardi BF, Bakhsh WR, Kautz H (2019) Natural language processing for the identification of surgical site infections in orthopaedics. J Bone Joint Surg Am 101, 2167–2174. [CrossRef] [PubMed] [Google Scholar]
- Farrow L, Zhong M, Anderson L (2024) Use of natural language processing techniques to predict patient selection for total hip arthroplasty: results from the AI to revolutionise the patient care pathway in hip and knee arthroplasty (ARCHERY) project. Orthop Proc 106-B, 34–34. [CrossRef] [Google Scholar]
- Farrow L, Zhong M, Anderson L (2024) Use of natural language processing techniques to predict patient selection for total hip and knee arthroplasty from radiology reports. Bone Joint J 106-B, 688–695. [CrossRef] [PubMed] [Google Scholar]
- Halawi MJ, Jongbloed W, Baron S, Savoy L, Williams VJ, Cote MP (2019) Patient dissatisfaction after primary total joint arthroplasty: the patient perspective. J Arthroplasty 34, 1093–1096. [CrossRef] [PubMed] [Google Scholar]
- Noble PC, Conditt MA, Cook KF, Mathis KB (2006) The John Insall Award: Patient expectations affect satisfaction with total knee arthroplasty. Clin Orthop Relat Res 452, 35–43. [CrossRef] [PubMed] [Google Scholar]
- Hunter J, Soleymani F, Viktor H, Michalowski W, Poitras S, Beaulé PE (2024) Using unsupervised machine learning to predict quality of life after total knee arthroplasty. J Arthroplasty 39, 677–682. [CrossRef] [PubMed] [Google Scholar]
- Kunze KN, Karhade AV, Sadauskas AJ, Schwab JH, Levine BR (2020) Development of machine learning algorithms to predict clinically meaningful improvement for the patient-reported health state after total hip arthroplasty. J Arthroplasty 35, 2119–2123. [CrossRef] [PubMed] [Google Scholar]
- Liu X, Liu Y, Lee ML, Hsu W, Liow MHL (2024) Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models. npj Digit Med 7, 266. [CrossRef] [PubMed] [Google Scholar]
- Nam HS, Ho JPY, Park SY, Cho JH, Lee YS (2023) The development of machine learning algorithms that can predict patients satisfaction using baseline characteristics, and preoperative and operative factors of total knee arthroplasty. Knee 44, 253–261. [CrossRef] [PubMed] [Google Scholar]
- Tolk JJ, Janssen RPA, Haanstra TM, Van der Steen MC, Bierma-Zeinstra SMA, Reijman M (2021) The influence of expectation modification in knee arthroplasty on satisfaction of patients: a randomized controlled trial. Bone Joint J 103-b, 619–626. [CrossRef] [PubMed] [Google Scholar]
- Batailler C, Shatrov J, Sappey-Marinier E, Servien E, Parratte S, Lustig S (2022) Artificial intelligence in knee arthroplasty: current concept of the available clinical applications. Arthroplasty 4, 17. [CrossRef] [PubMed] [Google Scholar]
- Harris AHS, Kuo AC, Weng Y, Trickey AW, Bowe T, Giori NJ (2019) Can machine learning methods produce accurate and easy-to-use prediction models of 30-day complications and mortality after knee or hip arthroplasty? Clin Orthop Relat Res 477, 452–460. [CrossRef] [PubMed] [Google Scholar]
- Jo C, Ko S, Shin WC, Han HS, Lee MC, Ko T, Ro DH (2020) Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm. Knee Surg Sports Traumatol Arthrosc 28, 1757–1764. [CrossRef] [PubMed] [Google Scholar]
- Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE (2019) Development and validation of a machine learning algorithm after primary total hip arthroplasty: applications to length of stay and payment models. J Arthroplasty 34, 632–637. [CrossRef] [PubMed] [Google Scholar]
- Arvind V, London DA, Cirino C, Keswani A, Cagle PJ (2021) Comparison of machine learning techniques to predict unplanned readmission following total shoulder arthroplasty. J Shoulder Elbow Surg 30, e50–e59. [CrossRef] [PubMed] [Google Scholar]
- Chen TL, Buddhiraju A, Costales TG, Subih MA, Seo HH, Kwon YM (2023) Machine learning models based on a national-scale cohort identify patients at high risk for prolonged lengths of stay following primary total hip arthroplasty. J Arthroplasty 38, 1967–1972. [CrossRef] [PubMed] [Google Scholar]
- Mohammadi R, Jain S, Namin AT, Scholem Heller M, Palacholla R, Kamarthi S, Wallace B (2020) Predicting unplanned readmissions following a hip or knee arthroplasty: retrospective observational study. JMIR Med Inform 8, e19761. [CrossRef] [PubMed] [Google Scholar]
- Park J, Zhong X, Miley EN, Rutledge RS, Kakalecik J, Johnson MC, Gray CF (2024) Machine learning-based predictive models for 90-day readmission of total joint arthroplasty using comprehensive electronic health records and patient-reported outcome measures. Arthroplasty Today 25, 101308. [CrossRef] [PubMed] [Google Scholar]
- Klemt C, Tirumala V, Habibi Y, Buddhiraju A, Chen TL, Kwon YM (2023) The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty. Arch Orthop Trauma Surg 143, 3279–3289. [Google Scholar]
- Zmistowski B, Karam JA, Durinka JB, Casper DS, Parvizi J (2013) Periprosthetic joint infection increases the risk of one-year mortality. J Bone Joint Surg Am 95, 2177–2184. [CrossRef] [PubMed] [Google Scholar]
- Eka A, Chen AF (2015) Patient-related medical risk factors for periprosthetic joint infection of the hip and knee. Ann Transl Med 3, 233. [PubMed] [Google Scholar]
- Chong YY, Chan PK, Chan VWK, Cheung A, Luk MH, Cheung MH, Fu H, Chiu KY (2023) Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review. Arthroplasty 5, 38. [CrossRef] [PubMed] [Google Scholar]
- Yeo I, Klemt C, Robinson MG, Esposito JG, Uzosike AC, Kwon YM (2023) The use of artificial neural networks for the prediction of surgical site infection following TKA. J Knee Surg 36, 637–643. [CrossRef] [PubMed] [Google Scholar]
- Kuo FC, Hu WH, Hu YJ (2022) Periprosthetic joint infection prediction via machine learning: comprehensible personalized decision support for diagnosis. J Arthroplasty 37, 132–141. [CrossRef] [PubMed] [Google Scholar]
- Lüftinger L, Ferreira I, Frank BJH, Beisken S, Weinberger J, von Haeseler A, Rattei T, Hofstaetter JG, Posch AE, Materna A (2021) Predictive antibiotic susceptibility testing by next-generation sequencing for periprosthetic joint infections: potential and limitations. Biomedicines 9, 910. [CrossRef] [PubMed] [Google Scholar]
- Parvizi J, Tan TL, Goswami K, Higuera C, Della Valle C, Chen AF, Shohat N (2018) The 2018 definition of periprosthetic hip and knee infection: an evidence-based and validated criteria. J Arthroplasty 33, 1309–1314.e1302. [CrossRef] [PubMed] [Google Scholar]
- Burn E, Edwards CJ, Murray DW, Silman A, Cooper C, Arden NK, Pinedo-Villanueva R, Prieto-Alhambra D (2018) Trends and determinants of length of stay and hospital reimbursement following knee and hip replacement: evidence from linked primary care and NHS hospital records from 1997 to 2014. BMJ Open 8, e019146. [CrossRef] [PubMed] [Google Scholar]
- Cilla M, Borgiani E, Martinez J, Duda GN, Checa S (2017) Machine learning techniques for the optimization of joint replacements: application to a short-stem hip implant. PLoS One 12, e0183755. [CrossRef] [PubMed] [Google Scholar]
- Jang SJ, Kunze KN, Brilliant ZR, Henson M, Mayman DJ, Jerabek SA, Vigdorchik JM, Sculco PK (2022) Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks: a deep learning radiological analysis. Bone Jt Open 3, 767–776. [CrossRef] [PubMed] [Google Scholar]
- Ding X, Zhang B, Li W, Huo J, Liu S, Wu T, Han Y (2021) Value of preoperative three-dimensional planning software (AI-HIP) in primary total hip arthroplasty: a retrospective study. J Int Med Res 49, 1–21. [Google Scholar]
- Velasquez Garcia A, Bukowiec LG, Yang L, Nishikawa H, Fitzsimmons JS, Larson AN, Taunton MJ, Sanchez-Sotelo J, O’Driscoll SW, Wyles CC (2024) Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review. Int Orthop 48, 997–1010. [CrossRef] [PubMed] [Google Scholar]
- Fernandes LR, Arce C, Martinho G, Campos JP, Meneghini RM (2023) Accuracy, reliability, and repeatability of a novel artificial intelligence algorithm converting two-dimensional radiographs to three-dimensional bone models for total knee arthroplasty. J Arthroplasty 38, 2032–2036. [CrossRef] [PubMed] [Google Scholar]
- Jung K, Kim H, Kholinne E, Park D, Choi H, Lee S, Shin M-J, Kim D-M, Hong J, Koh KH, Jeon I-H (2020) Navigation-assisted anchor insertion in shoulder arthroscopy: a validity study. BMC Musculoskelet Disord 21, 812. [CrossRef] [PubMed] [Google Scholar]
- Hu W, Tang J, Wu X, Zhang L, Ke B (2016) Minimally invasive versus open transforaminal lumbar fusion: a systematic review of complications. Int Orthop 40, 1883–1890. [CrossRef] [PubMed] [Google Scholar]
- Fan M, Liu Y, He D, Han X, Zhao J, Duan F, Liu B, Tian W (2020) Improved accuracy of cervical spinal surgery with robot-assisted screw insertion: a prospective, randomized, controlled study. Spine 45, 285–291. [CrossRef] [PubMed] [Google Scholar]
- Anthony CA, Rojas EO, Keffala V, Glass NA, Shah AS, Miller BJ, Hogue M, Willey MC, Karam M, Marsh JL (2020) Acceptance and commitment therapy delivered via a mobile phone messaging robot to decrease postoperative opioid use in patients with orthopedic trauma: randomized controlled trial. J Med Internet Res 22, e17750. [CrossRef] [PubMed] [Google Scholar]
- Wittig-Wells D, Higgins M, Carter J, Davis E, Holmes E, Jacob A, Samms-McPherson J, Simms S (2019) Impact of a preset daily cell phone alarm on medication adherence for aspirin as antithrombotic therapy. Orthop Nurs 38, 311–316. [CrossRef] [PubMed] [Google Scholar]
- Ramkumar PN, Haeberle HS, Navarro SM, Sultan AA, Mont MA, Ricchetti ET, Schickendantz MS, Iannotti JP (2018) Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit. J Shoulder Elbow Surg 27, 1198–1204. [CrossRef] [PubMed] [Google Scholar]
- Ramkumar PN, Haeberle HS, Ramanathan D, Cantrell WA, Navarro SM, Mont MA, Bloomfield M, Patterson BM (2019) Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplasty 34, 2253–2259. [CrossRef] [PubMed] [Google Scholar]
- Borjali A, Chen A, Muratoglu O, Morid M, Varadarajan K (2019) Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network. arXiv:1912.00943. [Google Scholar]
- Rouzrokh P, Ramazanian T, Wyles CC, Philbrick KA, Cai JC, Taunton MJ, Maradit Kremers H, Lewallen DG, Erickson BJ (2021) Deep learning artificial intelligence model for assessment of hip dislocation risk following primary total hip arthroplasty from postoperative radiographs. J Arthroplasty 36, 2197–2203. [CrossRef] [PubMed] [Google Scholar]
- Sadoghi P, Liebensteiner M, Agreiter M, Leithner A, Böhler N, Labek G (2013) Revision surgery after total joint arthroplasty: a complication-based analysis using worldwide arthroplasty registers. J Arthroplasty 28, 1329–1332. [CrossRef] [PubMed] [Google Scholar]
- Schroer WC, Berend KR, Lombardi AV, Barnes CL, Bolognesi MP, Berend ME, Ritter MA, Nunley RM (2013) Why are total knees failing today? Etiology of total knee revision in 2010 and 2011 J Arthroplasty 28, 116–119. [CrossRef] [PubMed] [Google Scholar]
- Bozic KJ, Kurtz SM, Lau E, Ong K, Vail TP, Berry DJ (2009) The epidemiology of revision total hip arthroplasty in the United States. J Bone Joint Surg Am 91, 128–133. [CrossRef] [PubMed] [Google Scholar]
- Kunutsor SK, Barrett MC, Beswick AD, Judge A, Blom AW, Wylde V, Whitehouse MR (2019) Risk factors for dislocation after primary total hip replacement: meta-analysis of 125 studies involving approximately five million hip replacements. Lancet Rheumatol 1, e111–e121. [CrossRef] [Google Scholar]
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