ArticleFederal practitioner : for the health care professionals of the VA, DoD, and PHS2020
Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.
Article in Federal practitioner : for the health care professionals of the VA, DoD, and PHS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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The trial behind it
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Who cites it
34 citing papers in PubMed.
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Artificial intelligence-based chest X-ray (AI-CXR) and hematology parameters to predict mortality and intubation events In COVID-19 patients at the second-referral hospital in Indonesia.BMC infectious diseases · 2026Article
- Improving the accuracy of emergency department clinicians in detecting SARS-COV-2 on chest X-rays using a bespoke virtual training platform.BMC medical education · 2025Article
- Understanding the neurological implications of acute and long COVID using brain organoids.Disease models & mechanisms · 2023Review
- Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.Scientific reports · 2023Article
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.Computers in biology and medicine · 2022Article
- Article
- Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology.Diagnostic pathology · 2022Article
- COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.European radiology · 2022Article
- COVID-19 in Kidney Transplant Recipient and Waitlist Patients: Implications of Chest Radiographic Severity Score.Journal of thoracic imaging · 2022Article
- Neuroimaging in the Era of Artificial Intelligence: Current Applications.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2022Review
- A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray.Diagnostics (Basel, Switzerland) · 2022Review
- Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.Journal of medical systems · 2022Article
- Limited generalizability of deep learning algorithm for pediatric pneumonia classification on external data.Emergency radiology · 2022Article
- Diagnostic Performance of a Deep Learning Model Deployed at a National COVID-19 Screening Facility for Detection of Pneumonia on Frontal Chest Radiographs.Healthcare (Basel, Switzerland) · 2022Article
- Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.PloS one · 2022Article
- CT Imaging Research Progress in COVID-19.Current medical imaging · 2022Review
- The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic.BJR open · 2022Review
- Artificial Intelligence and Precision Medicine: A Perspective.Advances in experimental medicine and biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans.
methodsIn this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision.
resultsOur trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value.
conclusionsWe have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.