Objectives
The 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.
Methods
All 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).
Results
In 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%).
Conclusions
Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network.

## Objectives
The 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.
### Methods
All 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).
### Results
In 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%).
### Conclusions
Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network.

Practical applications of deep learning: classifying the most common categories of plain radiographs in a PACS using a neural network

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)