Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs

Felix Busch, Keno K Bressem*, Phillip Suwalski, Lena Hoffmann, Stefan M Niehues, Denis Poddubnyy, Marcus R Makowski, Hugo J W L Aerts, Andrei Zhukov, Lisa C Adams

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Purpose: To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods: This institutional review board–approved retrospective study included patients with implantable pacemakers, cardio-verter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images. Using 2321 chest radiographs in 897 patients (median age, 76 years [range, 18–96 years]; 625 male, 272 female), CIEDs were categorized into four manufacturers, 27 models, and one “other” category. Five smartphones were used to acquire 11 072 images. Performance was reported using the Dice coefficient on the validation set for segmentation or balanced accuracy on the test set for manufacturer and model classification, respectively. Results: The segmentation tool achieved a mean Dice coefficient of 0.936 (IQR: 0.890–0.958). The model had an accuracy of 94.36% (95% CI: 90.93%, 96.84%; 251 of 266) for CIED manufacturer classification and 84.21% (95% CI: 79.31%, 88.30%; 224 of 266) for CIED model classification. Conclusion: The proposed deep learning model, trained on both traditional DICOM and smartphone images, showed high accuracy for segmentation and classification of CIEDs on chest radiographs.

Original languageEnglish
Article numbere230502
JournalRadiology: Artificial Intelligence
Volume6
Issue number5
Early online date17 Jul 2024
DOIs
Publication statusPublished - Sept 2024

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