Steganalysis of Medical Radiographs for Radiographic Machine Identification

Large online databases with radiographs from different institutions are being increasingly used in biomedical research consortiums. Radiographs from one machine at a single site may be suboptimal or corrupted. Steganalysis can be used to quality assurance of radiographs. Here, we use a deep learning framework for source identification of radiographs by predicting the exact radiographic machine (manufacturer and model) using a single rich model. A convolutional neural network architecture is applied to extract high-level content-free features, and three fully connected neural network layers are used to predict the radiographic machine source of the radiographs. Potential change in pixel information in medical images can be detected using steganalysis. Steganalysis contributes to maintaining trust in medical systems and ensures the accurate diagnosis and treatment of patients. Patients with cervical (n=2028 patients; 3905 radiographs) and chest (n=1499 patients; 2725 radiographs) radiographs obtained at Mayo Clinic from 01/01/2010 to 12/31/2021 were analyzed. Data was randomly split by patient into training/validation (n=80%) and test (n=20%) datasets respectively. The accuracy in the test dataset was 99.50% (AUC=99.72%) and 96.86% (AUC=98.23%) for cervical and chest radiographic machine identification respectively.