Pediatric distal humeral supracondylar fracture - achievement of optimal pinning configuration.

Journal and index pages often block iframe embedding. This reader keeps the evidence details in Orthonotes and leaves the source page one click away.

Abstract

[Indexed for MEDLINE] 16. Medicine (Baltimore). 2024 Jun 7;103(23):e38503. doi: 10.1097/MD.0000000000038503. Automatic diagnosis of pediatric supracondylar humerus fractures using radiomics-based machine learning. Yao W(1), Wang Y(1), Zhao X(2), He M(3), Wang Q(4), Liu H(1), Zhao J(1). Author information: (1)Department of Orthopedics, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China. (2)Department of Radiology, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China. (3)Department of Rehabilitation, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, PR China. (4)Department of Orthopedics, Tianjin Beichen Hospital, Tianjin, PR China. The aim of this study was to construct a classification model for the automatic diagnosis of pediatric supracondylar humerus fractures using radiomics-based machine learning. We retrospectively collected elbow joint Radiographs of children aged 3 to 14 years and manually delineated regions of interest (ROI) using ITK-SNAP. Radiomics features were extracted using pyradiomics, a python-based feature extraction tool. T-tests and the least absolute shrinkage and selection operator (LASSO) algorithm were used to further select the most valuable radiomics features. A logistic regression (LR) model was trained, with an 8:2 split into training and testing sets, and 5-fold cross-validation was performed on the training set. The diagnostic performance of the model was evaluated using receiver operating characteristic curves (ROC) on the testing set. A total of 411 fracture samples and 190 normal samples were included. 1561 features were extracted from each ROI. After dimensionality reduction screening, 40 and 94 features with the most diagnostic value were selected for further classification modeling in anteroposterior and lateral elbow radiographs. The area under the curve (AUC) of anteroposterior and lateral elbow radiographs is 0.65 and 0.72. Radiomics can extract and select the most valuable features from a large number of image features. Supervised machine-learning models built using these features can be used for the diagnosis of pediatric supracondylar humerus fractures. Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc. DOI: 10.1097/MD.0000000000038503 PMCID: PMC11155539