Artificial Intelligence Could Speed COVID-19 Detection, Treatment

Artificial intelligence has the potential to enhance the role of chest imaging and leverage large-scale data to quickly find solutions for detecting, containing, and treating COVID-19, according to a new report by a team at Johns Hopkins Medicine.

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Chest imaging is not currently a standard method for diagnosing COVID-19, but the technology has helped providers exclude other possible causes for COVID-19-like symptoms, confirming a diagnosis made by another means, or providing critical data for monitoring a patient’s progress.

Researchers argued that these efforts aren’t comprehensive enough, as AI has the power to enhance chest imaging beyond just screening for signs of COVID-19 in a patient’s lungs.

“Although chest imaging is not presently recommended for initial diagnosis of COVID-19 pneumonia, the scale of the COVID-19 pandemic calls for dedicated research on diagnostic and therapeutic approaches that are robust, reliable, and rapid. Data-driven AI applications could address this unmet need to allocate resources in a timely manner,” the team stated.

The team noted that AI could help improve risk stratification, categorizing patients for the type of care they receive based on the predicted course of their COVID-19 infection.

“As most cases of COVID-19 are mild, identifying severe and critical cases early is crucial. Currently, an open question is whether specific chest imaging features can predict hospital course,” the group wrote.  

“Multiple studies reported that CT findings may correlate with severity of symptoms, duration of illness, and even recovery. Furthermore, CT abnormalities may predate a positive RT-PCR test in both symptomatic and asymptomatic patients.”

Some research has identified some chest CT features and clinical characteristics that indicate worse prognosis in hospitalized patients, researchers said. Using AI and machine learning techniques, investigators have been able to categorize patients into mild and severe groups based on clinical criteria, allowing them to uncover more information about the impact of the virus.

In the future, AI could help researchers discover disease progression across different populations, such as in patients with chronic lung conditions and long-term smokers.

“More studies are needed in this area to determine whether certain features of lung pathology as assessed on computed tomography portend increased morbidity or mortality. Machine learning may indicate trends that predict ventilator requirement over the course of an ICU admission and imaging findings may play in important future role in risk stratification,” researchers said.

In addition to improved risk stratification, AI could enhance researchers’ understanding of the virus, leading them to better target and treat COVID-19. Investigators can use AI tools to understand the relationship between COVID-19 lung pathology and immune inflammation, for example.

“AI provides a prime opportunity for ‘data fusion’ of lung pathologic information with immunological information, especially on mapping the respective trajectories during hospitalization,” researchers stated.

“In the future, AI may help identify the immunological markers most associated with poor clinical course, which may yield new targets for immune modulation in therapeutic trials.”

AI has the potential to guide personalized treatments for patients with COVID-19 as well, the team said.

As new data and evidence about the disease continues to surface, researchers may find that different drugs are better suited for different sub-populations of patients or spectrum of clinical disease. AI could build complex models from broad sources of data to detect customized therapeutic targets.

“AI has successfully harnessed radiomics to characterize biological underpinnings of various disease processes, such as cancer, to predict response to treatment,” researchers said.

“Similarly, in COVID-19, if one or a few of these models can be validated prospectively, they could inform treatment algorithms and guidelines customized for patients along the spectrum of COVID-19 ranging from mild symptoms to death.”

Healthcare research teams have previously demonstrated the potential for AI to enhance COVID-19 detection. In May, a group from Mount Sinai published findings in Nature Medicine showing that an AI tool could quickly identify COVID-19 based on CT scans of the chest and patients’ clinical data.

“AI has huge potential for analyzing large amounts of data quickly, an attribute that can have a big impact in a situation such as a pandemic,” Zahi Fayad, PhD, Director of the BioMedical Engineering and Imaging Institute (BMEII) at the Icahn School of Medicine at Mount Sinai, said at the time. 

With the COVID-19 pandemic rapidly developing and changing every day, researchers should prioritize the creation and use of advanced tools like AI and machine learning to improve treatment and diagnosis.

“The medical community should promote a rapid development cycle for COVID-19-related AI while critically assessing risks in underlying methodology through existing biomedical imaging principles and practices,” researchers concluded.

“Coupled with a large and growing body of tools, AI has the potential to expand the role of chest imaging beyond diagnosis to facilitate risk stratification, treatment monitoring, and discovery of novel therapeutic targets in this global race to contain and treat COVID-19.”