Systematic review of machine-learning models in orthopaedic trauma: an overview and quality assessment of 45 studies

Systematic review of machine-learning models in orthopaedic trauma: an overview and quality assessment of 45 studies

Aims: Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of thi...

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Bibliographic Details
Main Authors: Hidde Dijkstra, Anouk van de Kuit, Tom de Groot, Olga Canta, Olivier Q. Groot, Jacobien H. Oosterhoff, Job N. Doornberg, On behalf of the Machine Learning Consortium
Format: Article
Language:English
Published: The British Editorial Society of Bone & Joint Surgery 2024-01-01
Series:Bone & Joint Open
Subjects:
systematic review
machine learning
orthopaedic trauma
quality assessment
clinicians
hip fracture
prognosis
orthopaedic trauma surgery
logistic regression techniques
imaging studies
anterior cruciate ligament
revision surgery
elective orthopaedic surgery
Online Access:https://online.boneandjoint.org.uk/doi/epdf/10.1302/2633-1462.51.BJO-2023-0095.R1

Internet

https://online.boneandjoint.org.uk/doi/epdf/10.1302/2633-1462.51.BJO-2023-0095.R1

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