FLC | Argonne, UChicago add medical images to COVID-19 research equation

Argonne, UChicago add medical images to COVID-19 research equation

May 15, 2020

With a grant from the new c3.ai Digital Transformation Institute, scientists from the University of Chicago and Argonne National Laboratory will develop new AI tools that use medical images to diagnose, monitor and help plan treatment for COVID-19 patients.

The new C3.ai Digital Transformation Institute (C3.ai DTI) unites six research universities, including the University of Chicago, with AI software provider C3.ai and Microsoft to focus scientific efforts on new technologies and urgent applications, such as mitigating the current COVID-19 pandemic.

The image analysis effort will be led by computer-aided diagnosis expert Maryellen Giger, the A.N. Pritzker Professor of Radiology at the University of Chicago, and will draw upon approaches in computer vision and deep learning from her UChicago lab, clinical lung imaging expertise from the university's Department of Radiology, and computational resources at Argonne. Because severe cases of COVID-19 most often present as a respiratory illness, triggering severe pneumonia in patients, chest X-rays and thoracic CT scans are often part of the medical dataset.

Once developed, Giger hopes that the system will help clinicians in several different aspects of diagnosing and treating the disease. First, it potentially could aid radiologists in spotting the disease, both in patients already suspected of COVID-19 and in those who undergo lung screening for other conditions. Second, the AI model could help physicians differentiate between different stages of the infection, guiding treatment choices for individual patients. As time goes on, researchers also hope the new AI method could help identify cases of COVID-19 that were missed due to lack of symptoms or unavailability of tests.

“There are multiple parts: the detection and diagnosis, as well as the response to treatment,” Giger said. “How do we follow the disease? And when do we see a presentation that says ‘start treatment’? And if they are being treated, how can we follow to determine if the treatment is working? Can we use this to inform decisions and treatment?”

The project builds upon Giger’s decades of experience in developing AI methods to analyze medical images and interpret signs of disease, including lung, breast, and prostate cancer, lupus, and bone conditions.

“We’re taking our AI development knowledge that we’ve learned from analyzing other lung diseases and using it here,” Giger said. “In doing so, we will conduct transfer learning and get away with a lot fewer cases as we combine medical imaging intelligence with machine intelligence.”

Like other clinical computer vision systems, the COVID-19 tool will be trained with real data to differentiate between chest images from patients with and without the infection. But this training typically requires large amounts of data, and in these early stages of the pandemic, COVID-19 medical images following patients from diagnosis through recovery remain scarce. The project will begin with a dataset from Chinese patients, and expand to include cases from UChicago Medicine and other health centers as it proceeds to a goal of 1,000 cases.

But it will also take advantage of their cutting-edge computational method known as cascade-based deep transfer learning to speed up this process. Instead of starting from scratch, they will start with network models Giger’s lab has developed for assessing other lung conditions and gradually retrain them to detect COVID-19 hallmarks. The approach carries clinical logic as well, as different stages of COVID-19 may present similar features to other lung diseases, such as cancer and other pneumonias.

Some of the image features used by the model in assessing severity of disease could also serve as important image-based biomarkers for use by clinicians to monitor disease progression or recovery.

Eventually, researchers hope the new AI method could help identify cases of COVID-19 that were missed due to lack of symptoms or unavailability of tests. Parallel AI methods from Giger’s group have looked at the “incidental” detection of emphysema, heart disease, and osteoporosis from thoracic CT scans gathered during lung cancer screening, as well as mesothelioma within her colleague’s lab. As coronavirus spreads through the population, these medical images may also reveal telltale signs of past or present COVID-19 infection, enabling treatment and providing valuable public health information.

“The impact of this research area is expected to be great because, as we move into a potential next wave or if COVID is looked at as a chronic disease, we will need to be able to recognize it,” Giger said. “One of the ways of doing that is through what's routinely obtained: chest radiographs and low dose CTs for lung screening. So this will just be another checkbox of ‘is there COVID potential here?’”

The UChicago project also includes Jonathan Chung, Vice Chair of Radiology for Quality and Section Chief of Cardiopulmonary Imaging at UChicago Medicine; Samuel Armato, Associate Professor of Radiology and Chair of the Committee on Medical Physics at UChicago Medicine; Ravi Madduri, Computational Scientist in the Mathematics and Computer Science division at Argonne; and Hui Li, Research Associate Professor in the Department of Radiology at UChicago Medicine.