Investigating Covid-19 pandemic-induced effects on detection of emergent clinical imaging findings by large-scale tracking of utilization and reading results for AI-based image analysis services

Axel Wismüller; Larry Stockmaster; M. Ali Vosoughi

doi:10.1117/12.2582307

15 February 2021 Investigating Covid-19 pandemic-induced effects on detection of emergent clinical imaging findings by large-scale tracking of utilization and reading results for AI-based image analysis services

Axel Wismüller, Larry Stockmaster, M. Ali Vosoughi

Author Affiliations +

Proceedings Volume 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging; 116000T (2021) https://doi.org/10.1117/12.2582307
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

We introduce a method for tracking results and utilization of Artificial Intelligence (tru-AI) based on machine learning applications in medical imaging, for analyzing pandemic-induced effects on healthcare systems. By tracking both large-scale utilization and AI results data, the tru-AI approach can establish surrogates for measuring the amount of care provided and estimate the prevalence of certain disease conditions under unusual circumstances, such as pandemic outbreaks. To quantitatively evaluate our approach, we analyzed service requests for automatically identifying intracranial hemorrhage (ICH) on head CT using a commercial AI solution (Aidoc, Tel Aviv, Israel). This software is typically used for AI-based prioritization of radiologists’ reading lists for reducing turnaround times in patients with emergent clinical findings, such as ICH or pulmonary embolism. Imaging data is anonymized, uploaded to a cloud-based inference machine in real time, and AI-based ICH detection results are returned. We recorded N = 3,084 emergency-setting non-contrast head CT studies at a major US healthcare system during two observation periods, namely (i) a pre-pandemic epoch (January 1–31, 2020) and (ii) after the Covid-19 outbreak (March 15 – April 30, 2020). Although daily counts of unique imaged patients were significantly lower during (37.9 ± 7.6) than before (42.0 ± 6.2) the Covid-19 outbreak, we found that ICH was more likely to be observed during than before the Covid-19 outbreak (p<0.05). Our results suggest that, by tracking both large-scale utilization and AI results data, the tru-AI approach can contribute clinical value as an exploratory tool, aiming at a better understanding of pandemic-related effects on healthcare.

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Axel Wismüller, Larry Stockmaster, and M. Ali Vosoughi "Investigating Covid-19 pandemic-induced effects on detection of emergent clinical imaging findings by large-scale tracking of utilization and reading results for AI-based image analysis services", Proc. SPIE 11600, Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, 116000T (15 February 2021); https://doi.org/10.1117/12.2582307

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