Predicting Surgical Versus Nonsurgical Management of Acute Isolated Distal Radius Fractures in Patients Under Age 60 Using a Convolutional Neural Network.
Authors
Affiliations (7)
Affiliations (7)
- Temerty Faculty of Medicine, University of Toronto, Ontario, Canada.
- Temerty Faculty of Medicine, University of Toronto, Ontario, Canada; Division of Plastic, Reconstructive and Aesthetic Surgery, Department of Surgery, University Health Network, Toronto Western Hand Program, Toronto, Ontario, Canada; Division of Orthopaedic Surgery, Department of Surgery, University Health Network, Toronto, Ontario, Canada.
- Division of Plastic, Reconstructive and Aesthetic Surgery, Department of Surgery, University Health Network, Toronto Western Hand Program, Toronto, Ontario, Canada.
- University Health Network Data Aggregation, Translation and Architecture Team, Toronto, Ontario, Canada.
- Temerty Faculty of Medicine, University of Toronto, Ontario, Canada; Division of Orthopaedic Surgery, Department of Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
- Temerty Faculty of Medicine, University of Toronto, Ontario, Canada; Division of General Surgery, Department of Surgery, University Health Network, Toronto, Ontario, Canada.
- Temerty Faculty of Medicine, University of Toronto, Ontario, Canada; Division of Plastic, Reconstructive and Aesthetic Surgery, Department of Surgery, University Health Network, Toronto Western Hand Program, Toronto, Ontario, Canada; Division of Orthopaedic Surgery, Department of Surgery, University Health Network, Toronto, Ontario, Canada. Electronic address: [email protected].
Abstract
Distal radius fractures (DRFs) represent up to 20% of the fractures in the emergency department. Delays to surgery of more than 14 days are associated with poorer functional outcomes and increased health care utilization/costs. At our institution, the average time to surgery is more than 19 days because of the separation of surgical and nonsurgical care pathways and a lengthy referral process. To address this challenge, we aimed to create a convolutional neural network (CNN) capable of automating DRF x-ray analysis and triaging. We hypothesize that this model will accurately predict whether an acute isolated DRF fracture in a patient under the age of 60 years will be treated surgically or nonsurgically at our institution based on the radiographic input. We included 163 patients under the age of 60 years who presented to the emergency department between 2018 and 2023 with an acute isolated DRF and who were referred for clinical follow-up. Radiographs taken within 4 weeks of injury were collected in posterior-anterior and lateral views and then preprocessed for model training. The surgeons' decision to treat surgically or nonsurgically at our institution was the reference standard for assessing the model prediction accuracy. We included 723 radiographic posterior-anterior and lateral pairs (385 surgical and 338 nonsurgical) for model training. The best-performing model (seven CNN layers, one fully connected layer, an image input size of 256 × 256 pixels, and a 1.5× weighting for volarly displaced fractures) achieved 88% accuracy and 100% sensitivity. Values for true positive (100%), true negative (72.7%), false positive (27.3%), and false negative (0%) were calculated. After training based on institution-specific indications, a CNN-based algorithm can predict with 88% accuracy whether treatment of an acute isolated DRF in a patient under the age of 60 years will be treated surgically or nonsurgically. By promptly identifying patients who would benefit from expedited surgical treatment pathways, this model can reduce times for referral.