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

By X. Mei, Hao-Chih Lee, Kai-yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu, Zongyu Xie, Yixuan Ma, P. Robson, M. Chung and 18 more. Nature Medicine, 2020

868 citations recorded by the source corpus.

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. Artificial intelligence algorithms integrating chest computed tomography scans and clinical information can diagnose COVID-19 with similar accuracy as compared to a senior radiologist.

Subfields: Health Informatics, Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4d0b7f66b75cb87ed3da5ffc024ff56b10303dcc

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In Infectious Diseases

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (2024, neutral): “Medical image segmentation plays an important role in modern clinical practice such as assisting in diagnoses, formulating treatment plans, and implementing therapies [2,20,27].”
Digital technologies in the public-health response to COVID-19 (2020, background): “…a digital ‘immunity passport’ to demonstrate protection from infection, although such a strategy is fraught with operational and clinical uncertainty 63,64 differentiation of COVID-19 from community-acquired pneumonia through the use of hospital chest scans by computerized tomography 65–67 .”
Swarm Learning for decentralized and confidential clinical machine learning (2021, methodology): “SL should be explored for image-based diagnosis of COVID-19 from patterns in X-ray images or CT scans 15,16 , structured health records 12 , or data from wearables for disease tracking 12 .”
A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics (2023, background): “In the second task, that is, adverse clinical outcome prediction for patients with COVID-19, the available clinical data were divided into four categories: demographics (age and gender), the structured chief complaint consisting of comorbidities (7) and symptoms (9) and laboratory test results (19) ”
COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings (2020, background): “Different AI approaches have recently been proposed to support the management of the pandemic [7, 8, 9, 10].”
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study (2020, background): “The only study, available so far in the peer-reviewed literature, that applied this approach, although in combination with CT-based diagnosis, was proposed in [32], but it was limited to white blood cell count.”
RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning (2022, methodology): “The COVID-19 dataset consists of 9050 chest CT images in patients with and without COVID-19 pneumonia (15).”
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020, background): “A number of studies have proposed deep learning systems based on chest CT imaging to distinguish COVID-19 cases from non-COVID-19 cases (which may include both normal and abnormal cases) [16, 20, 21, 22, 23, 24, 25, 26, 27, 17, 28, 29, 30].”