TY  - BOOK
AU  - Anderson, Cassidy C
AU  - Giladi, Aviram M
AU  - Grainger, Megan L
AU  - Mitchell, Abigail M
AU  - Sequeira, Sean 
TI  - Machine Learning Improves Functional Upper Extremity Use Capture in Distal Radius Fracture Patients
SN  - 2169-7574
PY  - 2022///
KW  - IN PROCESS -- NOT YET INDEXED
KW  - Curtis National Hand Center
KW  - MedStar Health Research Institute
KW  - MedStar National Rehabilitation Network
KW  - MedStar Union Memorial Hospital
KW  - Orthopaedic Surgery Residency
KW  - Orthopedics and Sports Medicine at Lafayette Center
KW  - Journal Article
N2  - Current outcome measures, including strength/range of motion testing, patient-reported outcomes (PROs), and motor skill testing, may provide inadequate granularity in reflecting functional upper extremity (UE) use after distal radius fracture (DRF) repair. Accelerometry analysis also has shortcomings, namely, an inability to differentiate functional versus nonfunctional movements. The objective of this study was to evaluate the accuracy of machine learning (ML) analyses in capturing UE functional movements based on accelerometry data for patients after DRF repair. In this prospective study, six patients were enrolled 2-6 weeks after DRF open reduction and internal fixation (ORIF). They all performed standardized activities while wearing a wrist accelerometer, and the data were analyzed by an ML algorithm. These activities were also videotaped and evaluated by visual inspection. Our novel ML algorithm was able to predict from accelerometry data whether the limb was performing a movement rated as functional, with accuracy of 90.4% +/- 3.6% for within-subject modeling and 79.8% +/- 8.9% accuracy for between-subject modeling. The application of ML algorithms to accelerometry data allowed for capture of functional UE activity in patients after DRF open reduction and internal fixation and accurately predicts functional UE use. Such analyses could improve our understanding of recovery and enhance routine postoperative rehabilitation in DRF patients. Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The American Society of Plastic Surgeons
UR  - https://dx.doi.org/10.1097/GOX.0000000000004472
ER  -