Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

Abstract

For diagnosis of coronavirus disease 2019 (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 2 d 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 patients with COVID-19. 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 radiological 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 patients who are positive for COVID-19. 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 SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR 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. The COVID-19 pandemic has rapidly propagated due to widespread person-to-person transmission 1-6. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific RT-PCR, but the test can take up to 2 d to complete. Chest CT is a valuable component of evaluation and diagnosis in symptomatic patients with

COVIDScreen: Explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays

“…RT-PCR testing is a time-consuming process and is currently available in limited supply which is leading to lower number of people getting tested daily [1]. The test may take up to 2 days to produce results [49]. In this duration, if the resources for isolation of suspected patients are unavailable, they may spread the virus to others, resulting in the proliferation of the virus.…”

Clinical deployment and validation of a radiology artificial intelligence system for COVID-19

“…To mitigate the burden on radiologists, while providing the highest quality care for patients, there has been tremendous effort to develop novel image processing approaches using machine learning algorithms 24 , particularly for COVID-19 diagnosis and prognosis 25 . These artificial intelligence (AI) models exploit and build upon medical imaging modalities such as chest CT scans [26][27][28][29][30][31][32] , chest radiographs [33][34][35][36][37][38][39][40] , and lung ultrasound 41 However, for any of these AI models to be useful in assisting clinicians in the care of COVID-19 patients, they require a robust and reliable AI deployment system 42 . Deployment is often a difficult step because clinical radiology infrastructure is not designed for easily embedding third-party systems, and doing so while maintaining context sensitivity and seamlessly embedding such systems into the radiologist workflow requires knowledge of hospital information system integration standards and often product-specific knowledge.…”

Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

“…Algorithms have already been proposed using advanced imaging, e.g. chest computed tomography (CT) (13,14). However, not all hospitals or countries can carry outa CT scan on every patient.…”