Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review
| العنوان: | Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review |
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| المؤلفون: | Jorrit-Jan Verlaan, Paul T Ogink, Aditya V. Karhade, F. Cumhur Oner, Michiel E.R. Bongers, Olivier Q. Groot, Joseph H. Schwab |
| المصدر: | Acta Orthopaedica, Vol 92, Iss 5, Pp 526-531 (2021) Acta Orthopaedica article-version (VoR) Version of Record |
| بيانات النشر: | Medical Journals Sweden, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | medicine.medical_specialty, Clinical Decision-Making, Machine learning, computer.software_genre, Outcome (game theory), Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Software, Predictive Value of Tests, medicine, Humans, Orthopedic Procedures, Orthopedics and Sports Medicine, 030212 general & internal medicine, Orthopedic surgery, 030222 orthopedics, business.industry, General Medicine, Range (mathematics), Surgery, Neural Networks, Computer, Artificial intelligence, business, computer, Predictive modelling, RD701-811, Research Article |
| الوصف: | Background and purpose — Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practice we evaluated which outcomes these new models have focused on and what methodologies are being employed. Material and methods — We performed a systematic search in PubMed, Embase, and Cochrane Library for studies published up to June 18, 2020. Studies reporting on non-ML prediction models or non-orthopedic outcomes were excluded. After screening 7,138 studies, 59 studies reporting on 77 prediction models were included. We extracted data regarding outcome, study design, and reported performance metrics. Results — Of the 77 identified ML prediction models the most commonly reported outcome domain was medical management (17/77). Spinal surgery was the most commonly involved orthopedic subspecialty (28/77). The most frequently employed algorithm was neural networks (42/77). Median size of datasets was 5,507 (IQR 635–26,364). The median area under the curve (AUC) was 0.80 (IQR 0.73–0.86). Calibration was reported for 26 of the models and 14 provided decision-curve analysis. Interpretation — ML prediction models have been developed for a wide variety of topics in orthopedics. Topics regarding medical management were the most commonly studied. Heterogeneity between studies is based on study size, algorithm, and time-point of outcome. Calibration and decision-curve analysis were generally poorly reported. |
| اللغة: | English |
| تدمد: | 1745-3682 1745-3674 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfa98bc7e18aa8a37d2dd24784cb8892 https://doaj.org/article/57cd23320fa348d0ac15ec15abb581d9 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....cfa98bc7e18aa8a37d2dd24784cb8892 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17453682 17453674 |
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