Machine Learning Improves Functional Upper Extremity Use Capture in Distal Radius Fracture Patients.

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Citation: Plastic and Reconstructive Surgery - Global Open. 10(8):e4472, 2022 Aug.PMID: 35999884Institution: Curtis National Hand Center | MedStar Health Research Institute | MedStar National Rehabilitation Network | MedStar Union Memorial HospitalDepartment: Orthopaedic Surgery Residency | Orthopedics and Sports Medicine at Lafayette CenterForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXEDYear: 2022ISSN:
  • 2169-7574
Name of journal: Plastic and reconstructive surgery. Global openAbstract: 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.All authors: Anderson CC, Geed S, Giladi AM, Grainger ML, Lum P, Mitchell AM, Sequeira SBFiscal year: FY2023Digital Object Identifier: Date added to catalog: 2022-10-20
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Journal Article MedStar Authors Catalog Article 35999884 Available 35999884

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.

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