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Artificial Intelligence in Health                                          Machine learning in arthroplasty



            68.  DeLude  JA, Bicknell RT, MacKenzie GA,  et al. An   utilizing machine learning.  J  Shoulder  Elbow  Surg.
               anthropometric study of the bilateral anatomy of the   2022;31(12):2449-2456.
               humerus. J Shoulder Elbow Surg. 2007;16(4):477-483.
                                                                  doi: 10.1016/j.jse.2022.07.013
               doi: 10.1016/j.jse.2006.09.016                  78.  Gowd AK, Agarwalla A, Amin NH,  et al. Construct
            69.  Tschannen M, Vlachopoulos L, Gerber C, Székely G,   validation of machine learning in the prediction of short-
               Fürnstahl P. Regression forest-based automatic estimation of   term postoperative complications following total shoulder
               the articular margin plane for shoulder prosthesis planning.   arthroplasty. J Shoulder Elbow Surg. 2019;28(12):e410-e421.
               Med Image Anal. 2016;31:88-97.                     doi: 10.1016/j.jse.2019.05.017
               doi: 10.1016/j.media.2016.02.008                79.  Lopez CD, Constant M, Anderson MJJ, Confino  JE,
            70.  Polce EM, Kunze KN, Fu MC,  et al. Development of   Heffernan JT, Jobin CM. Using machine learning methods
               supervised machine learning algorithms for prediction of   to predict nonhome discharge after elective total shoulder
               satisfaction at 2 years following total shoulder arthroplasty.   arthroplasty. JSES Int. 2021;5(4):692-698.
               J Shoulder Elbow Surg. 2021;30(6):e290-e299.       doi: 10.1016/j.jseint.2021.02.011
               doi: 10.1016/j.jse.2020.09.007                  80.  Kumazu Y, Kobayashi N, Kitamura N,  et al. Automated
            71.  Menendez ME, Shaker J, Lawler SM, Ring D, Jawa A.   segmentation by deep learning of loose connective tissue
               Negative patient-experience comments after total shoulder   fibers  to  define  safe  dissection  planes  in  robot-assisted
               arthroplasty. J Bone Joint Surg Am. 2019;101(4):330-337.  gastrectomy. Sci Rep. 2021;11(1):21198.
               doi: 10.2106/JBJS.18.00695                         doi: 10.1038/s41598-021-00557-3
            72.  McLendon PB, Christmas KN, Simon P,  et al. Machine   81.  Chen J, Oh PJ, Cheng N, et al. Use of automated performance
               learning can predict level of improvement in shoulder   metrics to measure surgeon performance during robotic
               arthroplasty. JB JS Open Access. 2021;6(1):e20.00128.  vesicourethral anastomosis and methodical development of
                                                                  a training tutorial. J Urol. 2018;200(4):895-902.
               doi: 10.2106/JBJS.OA.20.00128
                                                                  doi: 10.1016/j.juro.2018.05.080
            73.  Roche C, Kumar V, Overman S,  et al. Validation of a
               machine learning-derived clinical metric to quantify   82.  Ali S, Jonmohamadi Y, Fontanarosa D, Crawford R,
               outcomes after total shoulder arthroplasty. J Shoulder Elbow   Pandey  AK. One step surgical scene restoration for robot
               Surg. 2021;30(10):2211-2224.                       assisted minimally invasive surgery. Sci Rep. 2023;13(1):3127.
               doi: 10.1016/j.jse.2021.01.021                     doi: 10.1038/s41598-022-26647-4

            74.  Biron DR, Sinha I, Kleiner JE,  et al.  A  novel machine   83.  Wang F, Sun X, Li J. Surgical smoke removal via residual
               learning model developed to assist in patient selection for   Swin transformer network. Int J Comput Assist Radiol Surg.
               outpatient total shoulder arthroplasty. J  Am Acad Orthop   2023;18(8):1417-1427.
               Surg. 2020;28(13):e580-e585.                       doi: 10.1007/s11548-023-02835-z
               doi: 10.5435/JAAOS-D-19-00395                   84.  Capelli G, Verdi D, Frigerio I, et al. Artificial intelligence
            75.  Karnuta JM, Churchill JL, Haeberle HS,  et al. The value   surgery editorial board study group on ethics. White paper:
               of artificial neural networks for predicting length of stay,   Ethics and trustworthiness of artificial intelligence in clinical
               discharge disposition, and inpatient costs after anatomic   surgery. Art Int Surg. 2023;3:111-122.
               and reverse shoulder arthroplasty.  J  Shoulder Elbow Surg.      doi: 10.20517/ais.2023.04
               2020;29(11):2385-2394.
                                                               85.  Taher H, Grasso V, Tawfik S, Gumbs A. The challenges of
               doi: 10.1016/j.jse.2020.04.009                     deep learning in artificial intelligence and autonomous
            76.  Arvind V, London DA, Cirino C, Keswani A, Cagle   PJ.   actions in surgery: A  literature review.  Art Int Surg.
               Comparison of machine learning techniques to predict   2022;2:144-158.
               unplanned  readmission  following  total  shoulder     doi: 10.20517/ais.2022.11
               arthroplasty. J Shoulder Elbow Surg. 2021;30(2):e50-e59.
                                                               86.  Gumbs AA, Alexander F, Karcz K,  et al. White paper:
               doi: 10.1016/j.jse.2020.05.013                     Definitions of artificial intelligence and autonomous actions
                                                                  in clinical surgery. Art Int Surg. 2022;2:93-100.
            77.  Gowd AK, Agarwalla A, Beck EC,  et al. Prediction of
               total healthcare cost following total shoulder arthroplasty      doi: 10.20517/ais.2022.10







            Volume 2 Issue 2 (2025)                         28                               doi: 10.36922/aih.3278
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