Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model.

Autor: Anttila TT; Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Topeliuksenkatu 5B, Helsinki, 00260, Finland. turkka.anttila@helsinki.fi., Karjalainen TV; Department of Orthopedics, Traumatology and Hand Surgery, Central Finland Hospital, Jyvaskyla, Finland., Mäkelä TO; Medical Imaging Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.; Department of Physics, University of Helsinki, Helsinki, Finland., Waris EM; Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Topeliuksenkatu 5B, Helsinki, 00260, Finland., Lindfors NC; Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Topeliuksenkatu 5B, Helsinki, 00260, Finland., Leminen MM; Analytics and AI Development Services, IT Department, Helsinki University Hospital, Helsinki, Finland.; Department of Otorhinolaryngology and Phoniatrics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland., Ryhänen JO; Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, Topeliuksenkatu 5B, Helsinki, 00260, Finland.
Jazyk: angličtina
Zdroj: Journal of digital imaging [J Digit Imaging] 2023 Apr; Vol. 36 (2), pp. 679-687. Date of Electronic Publication: 2022 Dec 21.
DOI: 10.1007/s10278-022-00741-5
Abstrakt: Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture displacement are critical steps. The aim of this study was to use pixel-level annotations of fractures to develop a deep learning model for precise distal radius fracture detection. We randomly divided 3785 consecutive emergency wrist radiograph examinations from six hospitals to a training set (3399 examinations) and test set (386 examinations). The training set was used to develop the deep learning model and the test set to assess its validity. The consensus of three hand surgeons was used as the gold standard for the test set. The area under the ROC curve was 0.97 (CI 0.95-0.98) and 0.95 (CI 0.92-0.98) for examinations without a cast. Fractures were identified with higher accuracy in the postero-anterior radiographs than in the lateral radiographs. Our deep learning model performed well in our multi-hospital and multi-radiograph system manufacturer settings. Thus, segmentation-based deep learning models may provide additional benefit. Further research is needed with algorithm comparison and external validation.
(© 2022. The Author(s).)
Databáze: MEDLINE
Externí odkaz: