COVID-19 Impact on Radiology

COVID-19 Impact And Remote Radiology

What will the impact of COVID-19 be on radiology? During a webinar in April 2020 between Dicom Systems and neuroradiologist Dr. Peter Chang, we discussed the near-term and long-term shifts to the teleradiology world brought about by the COVID-19 pandemic.

The statements made in this blog reflect the publicly available data as of June 24, 2020.

COVID-19 has impacted healthcare all around the globe, and radiology is no exception. While many healthcare professionals are on the frontlines of treating COVID-19 patients and performing essential procedures, others have shifted to telemedicine as part of social distancing measures to help minimize the spread of the virus in the community. Yet another element of the pandemic is the acceleration of research for treatments that could improve patient outcomes for COVID-19 patients. In our recent webinar, Machine Learning in Emergency Radiology, Florent Saint-Clair spoke with Dr. Peter Chang, Neuroradiologist and Co-Founder of about the challenges and opportunities COVID-19 has created for teleradiology.

With the “Stay Home / Safer At Home / Lockdown” orders covering most of the country — and the world — for several weeks starting mid-March 2020, most non-urgent diagnostic procedures have been canceled and/or postponed. As a result, reports from the radiology community cite 50%-80%  decrease in imaging study volume since “Stay At Home” orders started keeping most patients at home. From mammograms to cardiac imaging, patients and practitioners are choosing to postpone imaging procedures until a time when it is considered safer. The American College of Radiology has created COVID-19 Resources for Radiologists to help radiology professionals make more informed decisions during the Coronavirus (COVID-19) pandemic.

Telemedicine and Teleradiology During The Pandemic

The rapid spread of coronavirus has highlighted the value of telemedicine solutions, as a means of limiting person-to-person contact. This includes the use of teleradiology tools to read images and communicate with referring physicians. Telemedicine allows for the continuity of care within healthcare facilities and patients, while also allowing for safe distance without sacrificing the quality of care. In the past, telehealth was primarily offered to patients in underserved areas and for follow-up appointments for patients with established care. However, the unprecedented pandemic situation has given physicians a runway to extend virtual care to a greater portion of the patient population.

As the coronavirus pandemic continues to spread, more provider organizations are embracing the remote radiology model, with radiologists working from home. And some in the field believe this trend could continue, even after COVID-19 is contained. Several advances in teleradiology have made this option possible for the long term: the healthcare cloud, vendor-neutral archives, and last but not least, AI.

Accelerated AI Research Approval and Funding

As is common in a crisis, we are seeing tremendous losses and suffering, but also some opportunities for innovation. In particular, research is receiving attention and increased funding. The traditional route to applying for funding is quite lengthy and involves grant preparation, proposal writing, and awaiting the decision. If your grant application is declined, you would need to re-apply and start the process all over. This could take years. The pandemic has upended the funding application process as researchers in both the public and the private sector race to find solutions to curb the spread of COVID-19. We have seen national funding for imaging-related AI initiatives, as well as a surge in collaborative nature and data sharing. In the webinar, Dr. Chang commented on the level of collaboration he is observing in the University of California system and academic research centers. The Institutional Review Boards (IRB) have seen unprecedented acceleration, with turnaround time as short as 1 week.

How AI Can Deliver Solutions To Fight  COVID-19

AI for radiology triage is often described as that ‘extra team member’, the one that sifts through the images and highlights areas of concern, prioritizing urgent cases so that the radiologist can adjust workflows to match patient needs. It is seen as a service that supports the radiologist and medical practitioner in providing access to on-demand analysis and sub-specialty services.

One use case for AI in radiology is the role of AI for Viral Pneumonia Diagnosis in Imaging. The model, known as COVID-19 detection neural network (COVNet), is able to detect COVID-19 and differentiate it from community-acquired pneumonia and other lung diseases using chest CT scans, according to the researchers. Imaging, in this case, is intended for clinical decision support, and not the first tool for COVID-19 diagnosis. It is also a tool for detecting changes.

The current predictions about the new post-pandemic reality include universal teleradiology at all hospitals and practices, coupled with fewer radiologists working on site.

  • Transforming a crisis into opportunity
  • Democratizing healthcare: scalability, minimal cost, and faster deployment
  • Circumventing bottlenecks for deployment or accepting new technology
  • A call to collaborate across domains, companies, and geographical borders

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