On the role of artificial intelligence in medical imaging of COVID-19

Jannis Born; David Beymer; Deepta Rajan; Adam Coy; Vandana V Mukherjee; Matteo Manica; Prasanth Prasanna; Deddeh Ballah; Michal Guindy; Dorith Shaham; Pallav L Shah; Emmanouil Karteris; Jan L Robertus; Maria Gabrani; Michal Rosen-Zvi

doi:10.1016/j.patter.2021.100269

On the role of artificial intelligence in medical imaging of COVID-19

Patterns (N Y). 2021 Jun 11;2(6):100269. doi: 10.1016/j.patter.2021.100269. Epub 2021 Apr 30.

Authors

Jannis Born^{1

2}, David Beymer³, Deepta Rajan³, Adam Coy^{3

4}, Vandana V Mukherjee³, Matteo Manica¹, Prasanth Prasanna^{5

3}, Deddeh Ballah^{6

3}, Michal Guindy^{7

8}, Dorith Shaham⁹, Pallav L Shah^{10

11

12}, Emmanouil Karteris¹³, Jan L Robertus^{10

12}, Maria Gabrani¹, Michal Rosen-Zvi^{14

15}

Affiliations

¹ IBM Research Europe, Zurich, Switzerland.
² Department for Biosystems Science & Engineering, ETH Zurich, Zurich, Switzerland.
³ IBM Almaden Research Center, San Jose, CA, USA.
⁴ Vision Radiology, Dallas, TX, USA.
⁵ Department of Radiology and Imaging Sciences, University of Utah Health Sciences Center, Salt Lake City, UT, USA.
⁶ Department of Radiology, Seton Medical Center, Daly City, CA, USA.
⁷ Assuta Medical Centres Radiology, Tel-Aviv, Israel.
⁸ Ben-Gurion University Medical School, Be'er Sheva, Israel.
⁹ Department of Radiology, Hadassah-Hebrew University Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
¹⁰ Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK.
¹¹ Chelsea & Westminster Hospital, London, UK.
¹² National Heart & Lung Institute, Imperial College London, London, UK.
¹³ College of Health, Medicine and Life Sciences, Brunel University London, London, UK.
¹⁴ IBM Research Haifa, Haifa, Israel.
¹⁵ Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.

Abstract

Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.

Keywords: COVID-19; Coronavirus; PRISMA; SARS-CoV-2; artificial intelligence; chest CT; chest X-ray; chest ultrasound; deep learning; digital healthcare; lung imaging; machine learning; medical imaging; meta-review.

Publication types

Review