Two new studies being presented this week at the annual meeting of the Radiological Society of North America (RSNA) address the potential risk of cyberattacks in medical imaging.
The Internet has been highly beneficial to healthcare—radiology included—improving access in remote areas, allowing for faster and better diagnoses, and vastly improving the management and transfer of medical records and images. However, increased connectivity can lead to increased vulnerability to outside interference.
Researchers and cybersecurity experts have begun to examine ways to mitigate the risk of cyberattacks in medical imaging before they become a real danger.
Medical imaging devices, such as X-ray, mammography, MRI, and CT machines, play a crucial role in diagnosis and treatment. As these devices are typically connected to hospital networks, they can be potentially susceptible to sophisticated cyberattacks, including ransomware attacks that can disable the machines. Due to their critical role in the emergency room, CT devices may face the greatest risk of cyberattack.
In a study presented Nov. 27, researchers from Ben-Gurion University of the Negev in Beer-Sheva, Israel, identified areas of vulnerability and ways to increase security in CT equipment. They demonstrated how a hacker might bypass security mechanisms of a CT machine in order to manipulate its behavior. Because CT uses ionizing radiation, changes to dose could negatively affect image quality, or—in extreme cases—pose harm to the patient.
For anomaly detection, the researchers developed a system using various advanced machine learning and deep learning methods, with training data consisting of actual commands recorded from real devices. The model learns to recognize normal commands and to predict if a new, unseen command is legitimate or not. If an attacker sends a malicious command to the device, the system will detect it and alert the operator before the command is executed.
A second study, to be presented Nov. 28, looked at the potential to tamper with mammogram results.
The researchers trained a cycle-consistent generative adversarial network (CycleGAN), a type of artificial intelligence application, on 680 mammographic images from 334 patients, to convert images showing cancer to healthy ones and to do the same, in reverse, for the normal control images. They wanted to determine if a CycleGAN could insert or remove cancer-specific features into mammograms in a realistic fashion.
The images were presented to three radiologists, who reviewed the images and indicated whether they thought the images were genuine or modified. None of the radiologists could reliably distinguish between the two.
