Artificial intelligence-enabled rapid diagnosis of COVID-19 patients

Abstract

For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARSCoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

Competing Interest Statement

Z.A.F discloses consulting fees from Alexion and GlaxoSmithKline; Research funding from Daiichi Sankyo; Amgen; Bristol Myers Squibb; Siemens Healthineers. Z.A.F receives financial compensation as a board member and advisor to Trained Therapeutix Discovery and owns equity in Trained Therapeutix Discovery as co-founder. A.B. is on the medical advisory board of RADLogics. B.P.L is an academic textbook author and associate editor for Elsevier, Inc. and receives royalties for his work. Other authors have no other competing interests to disclose.

Funding Statement

We thank the computational support by Biomedical Engineering and Imaging Institute at Icahn School of Medicine at Mount Sinai for this work. Z.A.F was funded by US National Institutes of Health grant NIH/NCRR CTSA UL1TR001433 and Mount Sinai COVID Informatics Center. V.M. received funding support from Astra Zeneca, Daiichi Sankyo, Alexion and Horizon Pharma. P.R. was supported by US National Institutes of Health (grant R01-HL071021). Y.Y. was supported by start-up seed fund from Icahn School of Medicine at Mount Sinai. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies had no role in study design, data collection and analysis, preparation of the manuscript, or the decision to publish.

Author Declarations

All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.

Yes

All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

The raw image dataset generated or analysed during the current study are not publicly available due to the dicom metadata containing information that could compromise patient privacy/consent. The models generated by this dataset are publicly available at https://github.com/howchihlee/COVID19_CT.