ArticleEuropean radiology2021
Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 11 citations in OpenAlex.
- A deep learning approach for projection and body-side classification in musculoskeletal radiographs.European radiology experimental · 2024Article
- Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network.Frontiers in immunology · 2023Article
- Deep Learning-driven classification of external DICOM studies for PACS archiving.European radiology · 2022Article
- AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?Skeletal radiology · 2022Review
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Authors and funding
9 authors at 3 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThe goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiological workflows.
methodsAll radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324).
resultsIn the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2-91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3-94.6%).
conclusionsUsing data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension. KEY POINTS: • Data from one single institution can be used to train a neural network for the correct detection of the 30 most common categories of plain radiographs. • The trained model achieved a high accuracy for the majority of categories and showed good generalizability to images from other institutions. • The neural network is made publicly available and can be used to automatically organize a PACS or to preselect radiographs so that they can be routed to more specialized neural networks for abnormality detection.
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