Radiograph Manufacturer and Model Identification Using Deep-RSI


  • Farid Ghareh Mohammadi Postdoctoral Research Fellow
  • Ronnie Sebro



Deep Learning, Medical Forensics, Lower Extremities, Radiology, Source Identifications


Malware attacks of healthcare institutions are simultaneously becoming more common and more sophisticated.  Artificial intelligence (AI) has resulted in the ability to rapidly alter or generate false images, advancing the ease of forgery of digital images. Digital image manipulation and substitution of radiographs are major threats to healthcare institutions because these altered images may affect patient care. Identifying the source (manufacturer, model) of radiology images is one method of validating the origin of radiology images in a healthcare system. In a previous study, researchers demonstrated that features from magnetic resonance imaging (MRI) could be used to trace and authenticate the source of the MRI images. We previously developed and tested the Deep learning for Radiograph Source Identification (Deep-RSI) approach for source identification of radiographs obtained of the upper extremities (hands, wrists, forearms, elbows, and shoulders). In this research, we present an empirical and quantitative investigation using deep learning to validate the source of digital radiographic images of the lower extremities (knees, legs, ankles, and feet). A convolutional neural network (CNN) is employed to extract features, which are then followed by three fully connected layers (FCNN). To ensure that our proposed method is a content-free approach, we added a new layer before the CNN to extract the initial content-free pixels and train the features using the CNN and FCNN layers. This proposed approach was used to identify the source of each digital image of a lower extremity. Adult patients of both sexes who had radiographs of the lower extremities at Mayo Clinic between 01/01/2010 and 12/31/2021 were evaluated. The data was randomly split by patient into training/validation and test datasets. There were 9 radiographic machine models and 6 manufacturers. Deep-RSI had an accuracy of 99.00% (AUC= 0.99) and 97.00% (AUC=0.94) for detecting the manufacturer and model of the radiographic machine for radiographs of the feet respectively, confirming that forensic evaluation of radiographs can be performed. This is the first medical forensics examination of this type to identify and confirm the source origins for radiographs of the lower extremities. This technique may be helpful to detect radiology malware attacks and scientific fraud.



Author Biography

Ronnie Sebro