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

Abstract

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. 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.

Deep Learning–driven classification of external DICOM studies for PACS archiving

“…This figure depicts one specific configuration of MOMO (the default configuration). It corresponds to the first algorithm in Table 2, labeled "NWE in layer 5," as the network ensemble is the fifth resource to be used current SOTA [7], and ultrasounds the lowest CI: 76.7-86.1%).…”

“…We achieved comparable performance across all modalities. Conventional radiographs achieved the highest prediction accuracy (97.1%, CI: 96.2–98.0%), outperforming the current SOTA [ 7 ], and ultrasounds the lowest (81.4%, CI: 76.7–86.1%).…”

“…We found that the neural networks offered a boost in predictive power and the best performance in our task was achieved with a variant of MOMO utilizing neural networks. The state of the art for comparable single series prediction is given by Dratsch et al [7] for plain radiographs and Raffy et al [8] for CT and MRI, while no Deep Learning bodyregion classification exists for ultrasound or X-ray angiography. Our training results compare favorably to the SOTA for radiographs.…”

“…In contrast to Dratsch et al [7], who used a neural network for evaluation of radiographs in the same context, the authors of this paper propose to extend the scope of the application to multiple modalities simultaneously-CT, X-ray angiography, radiographs, MRI, PET, ultrasound, and mammograms. Firstly, we aim to provide an automated classification algorithm, which can be integrated in the clinical routine.…”

“…Related works have performed body region classification using various Deep Learning strategies (see [7][8][9][10][11]), but none with the explicit goal of study classification for PACS archiving in mind, nor for this comprehensive list of modalities.…”