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Artificial Intelligence in Health                                          Machine learning in arthroplasty



            30.  Ramkumar PN, Karnuta JM, Navarro SM, et al. Preoperative   39.  Charilaou P, Battat R. Machine learning models and
               prediction of value metrics and a patient-specific payment   over-fitting considerations.  World J Gastroenterol.
               model for primary total hip arthroplasty: Development   2022;28(5):605-607.
               and validation of a deep learning model.  J  Arthroplasty.      doi: 10.3748/wjg.v28.i5.605
               2019;34(10):2228-2234.e1.
                                                               40.  Karnuta JM, Shaikh HJF, Murphy MP,  et al. Artificial
               doi: 10.1016/j.arth.2019.04.055
                                                                  intelligence for automated implant identification in knee
            31.  Ramkumar PN, Navarro SM, Haeberle HS, et al. Development   arthroplasty: A  multicenter external validation study
               and validation of a machine learning algorithm after primary   exceeding 3.5 million plain radiographs.  J  Arthroplasty.
               total hip arthroplasty: Applications to length of stay and   2023;38(10):2004-2008.
               payment models. J Arthroplasty. 2019;34(4):632-637.
                                                                  doi: 10.1016/j.arth.2023.03.039
               doi: 10.1016/j.arth.2018.12.030
                                                               41.  Yi PH, Wei J, Kim TK,  et al. Automated detection and
            32.  Magnéli M, Unbeck M, Rogmark C, Sköldenberg O,   classification of knee arthroplasty using deep learning. Knee.
               Gordon   M. Measuring adverse events following hip   2020;27(2):535-542.
               arthroplasty surgery using administrative data without
               relying on ICD-codes. PLoS One. 2020;15(11):e0242008.     doi: 10.1016/j.knee.2019.11.020
               doi: 10.1371/journal.pone.0242008               42.  Kunze KN, Polce EM, Patel A, Courtney PM, Sporer SM,
                                                                  Levine BR. Machine learning algorithms predict within
            33.  Shah AA, Devana SK, Lee C, Kianian R, van der Schaar M,   one size of the final implant ultimately used in total knee
               SooHoo NF. Development of a novel, potentially universal   arthroplasty with good-to-excellent accuracy.  Knee Surg
               machine learning algorithm for prediction of complications   Sports Traumatol Arthrosc. 2022;30(8):2565-2572.
               after total hip arthroplasty. J Arthroplasty. 2021;36(5):1655-
               1662.e1.                                           doi: 10.1007/s00167-022-06866-y
               doi: 10.1016/j.arth.2020.12.040                 43.  Kozic N, Weber S, Büchler P, et al. Optimisation of orthopaedic
                                                                  implant design using statistical shape space analysis based on
            34.  Harris  AHS,  Kuo  AC,  Weng  Y,  Trickey  AW,  Bowe  T,   level sets. Med Image Anal. 2010;14(3):265-275.
               Giori  NJ. Can machine learning methods produce accurate
               and easy-to-use prediction models of 30-day complications      doi: 10.1016/j.media.2010.02.008
               and mortality after knee or hip arthroplasty? Clin Orthop   44.  Lambrechts A, Wirix-Speetjens R, Maes F, Van Huffel S.
               Relat Res. 2019;477(2):452-460.                    Artificial intelligence based patient-specific preoperative
               doi: 10.1097/CORR.0000000000000601                 planning algorithm for total knee arthroplasty. Front Robot
                                                                  AI. 2022;9:840282.
            35.  Rouzrokh P, Ramazanian T, Wyles CC, et al. Deep learning
               artificial intelligence model for assessment of hip dislocation      doi: 10.3389/frobt.2022.840282
               risk following primary total hip arthroplasty from   45.  Farooq  H,  Deckard  ER,  Carlson  J,  Ghattas  N,
               postoperative radiographs. J Arthroplasty. 2021;36(6):2197-  Meneghini  RM. Coronal and sagittal component position
               2203.e3.                                           in contemporary total knee arthroplasty: Targeting native
               doi: 10.1016/j.arth.2021.02.028                    alignment optimizes clinical outcomes.  J  Arthroplasty.
                                                                  2023;38(7 Suppl 2):S245-S251.
            36.  Karhade AV, Schwab JH, Bedair HS. Development of
               machine learning algorithms for prediction of sustained      doi: 10.1016/j.arth.2023.04.040
               postoperative opioid prescriptions after total hip   46.  Farooq H, Deckard ER, Arnold NR, Meneghini RM.
               arthroplasty. J Arthroplasty. 2019;34(10):2272-2277.e1.  Machine learning algorithms identify optimal sagittal
               doi: 10.1016/j.arth.2019.06.013                    component position in total knee arthroplasty. J Arthroplasty.
                                                                  2021;36(7S):S242-S249.
            37.  Loppini M, Gambaro FM, Chiappetta K, Grappiolo G,
               Bianchi AM, Corino VDA. Automatic identification      doi: 10.1016/j.arth.2021.02.063
               of failure in hip replacement: An artificial intelligence   47.  Kim MS, Cho RK, Yang SC, Hur JH, In Y. Machine learning
               approach. Bioengineering (Basel). 2022;9(7):288.   for detecting total knee arthroplasty implant loosening on
               doi: 10.3390/bioengineering9070288                 plain radiographs. Bioengineering (Basel). 2023;10(6):632.
            38.  Shah RF, Bini  SA, Martinez AM, Pedoia V, Vail TP.      doi: 10.3390/bioengineering10060632
               Incremental inputs improve the automated detection of   48.  Mehta B, Goodman S, DiCarlo E, et al. Machine learning
               implant loosening using machine-learning algorithms. Bone   identification of thresholds to discriminate osteoarthritis
               Joint J. 2020;102-B(6_Supple_A):101-106.           and  rheumatoid  arthritis  synovial  inflammation.  Arthritis
               doi: 10.1302/0301-620X.102B6.BJJ-2019-1577.R1      Res Ther. 2023;25(1):31.


            Volume 2 Issue 2 (2025)                         26                               doi: 10.36922/aih.3278
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