Dataset for Segmentation and Classification of Cardiac Implantable Electronic Devices in Chest X-Rays

This dataset encompasses a comprehensive collection of 2,321 chest radiographs
(originally DICOM, converted to PNG) and 11,072 smartphone images documenting
various cardiac implantable electronic devices (CIEDs) such as implantable
pacemakers, cardioverter defibrillators, cardiac resynchronization therapy
devices, and cardiac monitors, collected from 896 patients at the Charite \-
Universitatsmedizin Berlin over a decade (January 2012 to January 2022). The
dataset's primary objective is to advance the development and validation of
automated techniques for precise segmentation and classification of CIEDs
across both traditional and smartphone-generated imagery. It includes a
diverse array of 27 CIED models from four manufacturers (Medtronic, Biotronic,
Boston Scientific, Abbott Laboratories including St. Jude Medical), captured
in anterior-posterior or posterior-anterior chest radiographs using five
different smartphone brands. This dataset aims to underpin the creation of
deep learning models for accurately identifying CIED types, manufacturers, and
models. Designed to be a robust foundation for research and clinical
applications, it facilitates the exploration of innovative machine learning
solutions for CIED identification leveraging both original radiogrpahs and
smartphone images, thereby addressing a critical need in cardiac care
technology.