Paediatric upper limb fracture healing time prediction using a machine learning approach

All Life

Sorayya Malek¹  Firdaus Aziz¹  Roshan Gunalan¹  A Saw¹  WH Chee¹  Chia Fong Lau¹ 

[1] University of Malaya;

关键词: upper limb;    paediatric orthopaedic;    support vector regression;    random forest;    self-organising maps;    machine learning;   

DOI  :  10.1080/26895293.2022.2064923

来源: DOAJ

【 摘 要 】

To analyse and predict the healing time of upper limb fractures in children, machine learning algorithms were used. Paediatric orthopaedic data was obtained from the University Malaya Medical Centre. The data set includes radiographs of upper limb fractures involving the radius, ulna, and humerus in children under the age of twelve, with ages recorded from the date and time of initial injury. Inputs assessment included: age, gender, bone type, the number of bones involved, fracture type, angulation and the distance of the fracture. Random Forest (RF) and Support Vector Regression (SVR) algorithms were used to predict and identify variables associated with fracture healing time. Self Organizing Maps was then used for visualization and ordination of factors associated with healing time. Algorithms performance was measured using root mean square error (RMSE). A significant determinant in fracture healing includes age, bone part, fracture angulation, and distance. The Wilcoxon signed ranked test reported there is a significant difference between the prediction result of the SVR model (RMSE = 2.56) and the RF model (RMSE =  2.66). Predicting healing time can be used in the treatment process and follow up period for general practitioners and medical officers. The algorithm is deployed online at https://kidsfractureexpert.com/.

【 授权许可】

Unknown