Monday, November 29 | 1:30 p.m.-2:30 p.m. | SSPH05-2 | Room TBA
In this session, researchers will discuss the potential of artificial intelligence (AI) for supporting treatment decisions in COVID-19 patients.Making use of a small dataset of 41 COVID-19 cases, researchers from the University of Chicago had previously developed a deep-learning algorithm to predict from CT scans if patients would need corticosteroids -- the primary treatment for severe COVID-19. In this study, they sought to test their model on a larger cohort of 916 patients that included exams acquired on scanners from one of five different vendors: FMI, GE Healthcare, Philips Healthcare, Siemens Healthineers, and United Imaging.
Although overall performance decreased, from an area under the curve (AUC) of 0.85 on the small dataset to 0.68 on the larger dataset, it still reached statistical significance (p = 0.002) compared with random chance (AUC = 0.5). Delving further into the results, the researchers also found that the algorithm's performance varied by scanner manufacturer, ranging from an AUC of 0.52 to 0.64.
Although the algorithm yielded lower performance on the larger dataset, this finding could be attributed to the limited training data and the greater variety of scanner manufacturers utilized in the study, according to the authors.
"Classification performances showed statistically significant performance, indicating strong potential for quantitative CT in informing steroid treatments with performance varying across scanner manufacturers," they concluded.
Stop by this Monday talk by doctoral student Jordan Fuhrman to learn more.
![Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/06/hanneman.r6SMLzkezo.png?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
