ArticleInsights into imaging2019
Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs.
Article in Insights into imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep Learning-Based Automated Computed Tomography Classification of Posterior Malleolar Fractures for a Standardized Diagnostic Framework.Journal of imaging informatics in medicine · 2026Article
- Large language model-based uncertainty-adjusted label extraction for artificial intelligence model development in upper extremity radiography.European radiology · 2026Article
- Technical requirements and optimization strategies for home-based teleradiology workstations: a review article.Insights into imaging · 2025Review
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports.Insights into imaging · 2024Article
- Artificial intelligence in foot and ankle surgery: current concepts.Orthopadie (Heidelberg, Germany) · 2023Review
- ESR paper on structured reporting in radiology-update 2023.Insights into imaging · 2023Article
- Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review.Foot & ankle orthopaedics · 2023Article
- Deep learning to detect anterior cruciate ligament tear on knee MRI: multi-continental external validation.European radiology · 2022Article
- Radiology reporting in rectal cancer using MRI: adherence to national template for structured reporting.Acta radiologica (Stockholm, Sweden : 1987) · 2022Article
- [Structured reporting in radiology : German and European radiology societies' point of view].Der Radiologe · 2021Review
- Collection and Reporting of Patient-reported Outcome Measures in Arthroplasty Registries: Multinational Survey and Recommendations.Clinical orthopaedics and related research · 2021Article
- Digital twins, artificial intelligence, and machine learning technology to identify a real personalized motion axis of the tibiotalar joint for robotics in total ankle arthroplasty.International orthopaedics · 2021Article
- Structured reporting of brain MRI following mechanical thrombectomy in acute ischemic stroke patients.BMC medical imaging · 2021Article
- Contextual Structured Reporting in Radiology: Implementation and Long-Term Evaluation in Improving the Communication of Critical Findings.Journal of medical systems · 2020Article
- Bridging the "last mile" gap between AI implementation and operation: "data awareness" that matters.Annals of translational medicine · 2020Review
- The role of structured reporting and structured operation planning in functional endoscopic sinus surgery.PloS one · 2020Article
- Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData used for training of deep learning networks usually needs large amounts of accurate labels. These labels are usually extracted from reports using natural language processing or by time-consuming manual review. The aim of this study was therefore to develop and evaluate a workflow for using data from structured reports as labels to be used in a deep learning application. MATERIALS AND
methodsWe included all plain anteriorposterior radiographs of the ankle for which structured reports were available. A workflow was designed and implemented where a script was used to automatically retrieve, convert, and anonymize the respective radiographs of cases where fractures were either present or absent from the institution's picture archiving and communication system (PACS). These images were then used to retrain a pretrained deep convolutional neural network. Finally, performance was evaluated on a set of previously unseen radiographs.
resultsOnce implemented and configured, completion of the whole workflow took under 1 h. A total of 157 structured reports were retrieved from the reporting platform. For all structured reports, corresponding radiographs were successfully retrieved from the PACS and fed into the training process. On an unseen validation subset, the model showed a satisfactory performance with an area under the curve of 0.850 (95% CI 0.634-1.000) for detection of fractures.
conclusionWe demonstrate that data obtained from structured reports written in clinical routine can be used to successfully train deep learning algorithms. This highlights the potential role of structured reporting for the future of radiology, especially in the context of deep learning.
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