COVID-19 Story Tip: Artificial Intelligence Can Improve How Chest Images Are Used in Care of COVID-19 Patients
Within the study, published in the May 6 issue of Radiology: Artificial Intelligence, the researchers say that “AI’s power to generate models from large volumes of information — fusing molecular, clinical, epidemiological and imaging data — may accelerate solutions to detect, contain and treat COVID-19.”
Although CT chest imaging is not currently a routine method for diagnosing COVID-19 in patients, it has been helpful in excluding other possible causes for COVID-like symptoms, confirming a diagnosis made by another means or providing critical data for monitoring a patient’s progress in severe cases of the disease. The Johns Hopkins Medicine researchers believe this isn’t enough, making the case that there is “an untapped potential” for AI-enhanced imaging to improve. They suggest the technology can be used for:
- Risk stratification, the process of categorizing patients for the type of care they receive based on the predicted course of their COVID-19 infection.
- Treatment monitoring to define the effectiveness of agents used to combat the disease.
- Modeling how COVID-19 behaves, so that novel, customized therapies can be developed, tested and deployed.
For example, the researchers propose that “AI may help identify the immunological markers most associated with poor clinical course, which may yield new targets” for drugs that will direct the immune system against the SARS-CoV-2 virus that causes COVID-19.
Shinjini Kundu, M.D., Ph.D., a radiology resident at the Johns Hopkins University School of Medicine, is available to discuss the use of AI to make chest imaging a more beneficial tool in the care of COVID-19 patients.
For information from Johns Hopkins Medicine about the coronavirus pandemic, visit hopkinsmedicine.org/coronavirus. For information on the coronavirus from throughout the Johns Hopkins enterprise, including the Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University, visit coronavirus.jhu.edu.