Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?

被引：0

作者：

Franceschetti E. ^{[1
,2
]}

Gregori P. ^{[1
,2
]}

De Giorgi S. ^{[1
,2
]}

Martire T. ^{[1
,2
]}

Za P. ^{[1
,2
]}

Papalia G.F. ^{[1
,2
]}

Giurazza G. ^{[1
,2
]}

Longo U.G. ^{[1
,2
]}

Papalia R. ^{[1
,2
]}

机构：

[1] Fondazione Policlinico Universitario, Campus Bio-Medico, Roma (RM)

[2] Research Unit of Orthopaedic and Trauma Surgery, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome

来源：

MUSCULOSKELETAL SURGERY | 2024年 / 108卷 / 2期

关键词：

Anterior elevation; Artificial intelligence; Machine learning; Predicting results; Predicting shoulder arthroplasty outcomes; rTSA;

D O I：

10.1007/s12306-023-00811-z

中图分类号：

学科分类号：

摘要：

The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients who underwent reverse shoulder arthroplasty at the same institute have been collected with the purpose of generating algorithms which could predict the target. Twenty-eight features were extracted and applied to two different machine learning techniques: Linear regression and support vector regression (SVR). These two techniques were also compared in order to define to most faithfully predictive. Using the extracted features, the SVR algorithm resulted in a mean absolute error (MAE) of 11.6° and a classification accuracy (PCC) of 0.88 on the test-set. Linear regression, instead, resulted in a MAE of 13.0° and a PCC of 0.85 on the test-set. Our machine learning study demonstrates that machine learning could provide high predictive algorithms for anterior elevation after reverse shoulder arthroplasty. The differential analysis between the utilized techniques showed higher accuracy in prediction for the support vector regression. Level of Evidence III: Retrospective cohort comparison; Computer Modeling. © The Author(s), under exclusive licence to Istituto Ortopedico Rizzoli 2024.

引用

页码：163 / 171

页数：8

共 25 条

[1] Currie G., Hawk K.E., Rohren E., Et al., Machine learning and deep learning in medical imaging: intelligent imaging, J Med Imag Radiat Sci, 50, pp. 477-487, (2019)
[2] Handelman G.S., Kok H.K., Chandra R.V., Et al., eDoctor: machine learning and the future of medicine, J Intern Med, 284, pp. 603-619, (2018)
[3] Jordan M.I., Mitchell T.M., Machine learning: trends, perspectives, and prospects, Science, 349, pp. 255-260, (2015)
[4] Weiss J., Kuusisto F., Boyd K., Et al., Machine learning for treatment assignment: improving individualized risk attribution, AMIA Annu Symp Proc, 2015, pp. 1306-1315, (2015)
[5] Cabitza F., Locoro A., Banfi G., Machine learning in orthopedics: a literature review, Front Bioeng Biotechnol, 6, (2018)
[6] Lalehzarian S.P., Gowd A.K., Liu J.N., Machine learning in orthopaedic surgery, WJO, 12, 685, (2021)
[7] Myers T.G., Ramkumar P.N., Ricciardi B.F., Et al., Artificial intelligence and orthopaedics: an introduction for clinicians, J Bone Joint Surg, 102, pp. 830-840, (2020)
[8] Gowd A.K., Agarwalla A., Amin N.H., Et al., Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty, J Shoulder Elbow Surg, 28, pp. e410-e421, (2019)
[9] Roche C., Kumar V., Overman S., Et al., Validation of a machine learning–derived clinical metric to quantify outcomes after total shoulder arthroplasty, J Shoulder Elbow Surg, 30, pp. 2211-2224, (2021)
[10] Saenz G., de Tejada M., Escobar A., Bilbao A., Et al., A prospective study of the association of patient expectations with changes in health-related quality of life outcomes, following total joint replacement, BMC Musculoskelet Disord, 15, (2014)

← 1 2 3 →