Abstract
SummaryAlthough 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.
Highlights
The COVID-19 pandemic has created a desperate need for fast, ubiquitous, accurate, and low-cost tests, and lung imaging is a key complementary tool in the diagnosis and management of COVID-19.1,2 According to the American College of Radiology (ACR) and the Fleischner Society Consensus Statement, imaging of COVID-19 is indicated in case of worsening respiratory symptoms, and, in a resource-constrained environment, for triage of patients with moderate to severe clinical features and a high probability of disease.[3,4]
Progress in artificial intelligence (AI) for medical imaging In recent years, AI solutions have shown to be capable of assisting radiologists and clinicians in detecting diseases, assessing severity, automatically localizing and quantifying disease features, or providing an automated assessment of disease prognosis
Lung and breast imaging comparison To enable a perspective on the emergence of AI for medical imaging (MI) of COVID19, we have compiled a comparison of the progress of automatic analysis in breast and lung imaging, as defined in the literature above, from between 2017 and 2020
Summary
The COVID-19 pandemic has created a desperate need for fast, ubiquitous, accurate, and low-cost tests, and lung imaging is a key complementary tool in the diagnosis and management of COVID-19.1,2 According to the American College of Radiology (ACR) and the Fleischner Society Consensus Statement, imaging of COVID-19 is indicated in case of worsening respiratory symptoms, and, in a resource-constrained environment, for triage of patients with moderate to severe clinical features and a high probability of disease.[3,4] This involves two main tasks. The field of artificial intelligence (AI) in medical imaging (MI) is growing in the context of COVID-19,5–7 and hopes are high that AI can support clinicians and radiologists on these tasks. We review the current progress in the development of AI technologies for MI to assist in addressing the COVID-19 pandemic, discuss how AI meets the identified gaps, and share observations regarding the maturity and clinical relevancy of these developments
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