New AI Platform Detects COVID-19 on Chest X-Rays with Accuracy and Speed
By MedImaging International staff writers Posted on 25 Nov 2020 |

Image: Generated heatmaps appropriately highlighted abnormalities in the lung fields in those images accurately labeled as COVID-19 positive (A-C) in contrast to images which were accurately labeled as negative for COVID-19 (D). Intensity of colors on the heatmap correspond to features of the image that are important for prediction of COVID-19 positivity (Photo courtesy of Northwestern University)
A new artificial intelligence (AI) platform that detects COVID-19 by analyzing X-ray images of the lungs is about 10 times faster as well as 1-6% more accurate than individual specialized radiologists.
Called DeepCOVID-XR, the machine-learning algorithm developed by researchers at the Northwestern University (Evanston, IL, USA) outperformed a team of specialized thoracic radiologists - spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the AI system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The researchers also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19.
To develop, train and test the new algorithm, the researchers used 17,002 chest X-ray images - the largest published clinical dataset of chest X-rays from the COVID-19 era used to train an AI system. The team then tested DeepCOVID-XR against five experienced cardiothoracic fellowship-trained radiologists on 300 random test images. Each radiologist took approximately two-and-a-half to three-and-a-half hours to examine this set of images, whereas the AI system took about 18 minutes. The radiologists' accuracy ranged from 76-81%. DeepCOVID-XR performed slightly better at 82% accuracy. The researchers have made the algorithm publicly available with hopes that others can continue to train it with new data. Right now, DeepCOVID-XR is still in the research phase, but could potentially be used in the clinical setting in the future.
"We are not aiming to replace actual testing," said Northwestern's Aggelos Katsaggelos, an AI expert and senior author of the study. "X-rays are routine, safe and inexpensive. It would take seconds for our system to screen a patient and determine if that patient needs to be isolated."
"It could take hours or days to receive results from a COVID-19 test," said Dr. Ramsey Wehbe, a cardiologist and postdoctoral fellow in AI at the Northwestern Medicine Bluhm Cardiovascular Institute. "AI doesn't confirm whether or not someone has the virus. But if we can flag a patient with this algorithm, we could speed up triage before the test results come back."
Related Links:
Northwestern University
Called DeepCOVID-XR, the machine-learning algorithm developed by researchers at the Northwestern University (Evanston, IL, USA) outperformed a team of specialized thoracic radiologists - spotting COVID-19 in X-rays about 10 times faster and 1-6% more accurately. The researchers believe physicians could use the AI system to rapidly screen patients who are admitted into hospitals for reasons other than COVID-19. Faster, earlier detection of the highly contagious virus could potentially protect health care workers and other patients by triggering the positive patient to isolate sooner. The researchers also believe the algorithm could potentially flag patients for isolation and testing who are not otherwise under investigation for COVID-19.
To develop, train and test the new algorithm, the researchers used 17,002 chest X-ray images - the largest published clinical dataset of chest X-rays from the COVID-19 era used to train an AI system. The team then tested DeepCOVID-XR against five experienced cardiothoracic fellowship-trained radiologists on 300 random test images. Each radiologist took approximately two-and-a-half to three-and-a-half hours to examine this set of images, whereas the AI system took about 18 minutes. The radiologists' accuracy ranged from 76-81%. DeepCOVID-XR performed slightly better at 82% accuracy. The researchers have made the algorithm publicly available with hopes that others can continue to train it with new data. Right now, DeepCOVID-XR is still in the research phase, but could potentially be used in the clinical setting in the future.
"We are not aiming to replace actual testing," said Northwestern's Aggelos Katsaggelos, an AI expert and senior author of the study. "X-rays are routine, safe and inexpensive. It would take seconds for our system to screen a patient and determine if that patient needs to be isolated."
"It could take hours or days to receive results from a COVID-19 test," said Dr. Ramsey Wehbe, a cardiologist and postdoctoral fellow in AI at the Northwestern Medicine Bluhm Cardiovascular Institute. "AI doesn't confirm whether or not someone has the virus. But if we can flag a patient with this algorithm, we could speed up triage before the test results come back."
Related Links:
Northwestern University
Latest Radiography News
- World's Largest Class Single Crystal Diamond Radiation Detector Opens New Possibilities for Diagnostic Imaging
- AI-Powered Imaging Technique Shows Promise in Evaluating Patients for PCI
- Higher Chest X-Ray Usage Catches Lung Cancer Earlier and Improves Survival
- AI-Powered Mammograms Predict Cardiovascular Risk
- Generative AI Model Significantly Reduces Chest X-Ray Reading Time
- AI-Powered Mammography Screening Boosts Cancer Detection in Single-Reader Settings
- Photon Counting Detectors Promise Fast Color X-Ray Images
- AI Can Flag Mammograms for Supplemental MRI
- 3D CT Imaging from Single X-Ray Projection Reduces Radiation Exposure
- AI Method Accurately Predicts Breast Cancer Risk by Analyzing Multiple Mammograms
- Printable Organic X-Ray Sensors Could Transform Treatment for Cancer Patients
- Highly Sensitive, Foldable Detector to Make X-Rays Safer
- Novel Breast Cancer Screening Technology Could Offer Superior Alternative to Mammogram
- Artificial Intelligence Accurately Predicts Breast Cancer Years Before Diagnosis
- AI-Powered Chest X-Ray Detects Pulmonary Nodules Three Years Before Lung Cancer Symptoms
- AI Model Identifies Vertebral Compression Fractures in Chest Radiographs
Channels
Radiography
view channel
World's Largest Class Single Crystal Diamond Radiation Detector Opens New Possibilities for Diagnostic Imaging
Diamonds possess ideal physical properties for radiation detection, such as exceptional thermal and chemical stability along with a quick response time. Made of carbon with an atomic number of six, diamonds... Read more
AI-Powered Imaging Technique Shows Promise in Evaluating Patients for PCI
Percutaneous coronary intervention (PCI), also known as coronary angioplasty, is a minimally invasive procedure where small metal tubes called stents are inserted into partially blocked coronary arteries... Read moreMRI
view channel
AI Tool Tracks Effectiveness of Multiple Sclerosis Treatments Using Brain MRI Scans
Multiple sclerosis (MS) is a condition in which the immune system attacks the brain and spinal cord, leading to impairments in movement, sensation, and cognition. Magnetic Resonance Imaging (MRI) markers... Read more
Ultra-Powerful MRI Scans Enable Life-Changing Surgery in Treatment-Resistant Epileptic Patients
Approximately 360,000 individuals in the UK suffer from focal epilepsy, a condition in which seizures spread from one part of the brain. Around a third of these patients experience persistent seizures... Read more
AI-Powered MRI Technology Improves Parkinson’s Diagnoses
Current research shows that the accuracy of diagnosing Parkinson’s disease typically ranges from 55% to 78% within the first five years of assessment. This is partly due to the similarities shared by Parkinson’s... Read more
Biparametric MRI Combined with AI Enhances Detection of Clinically Significant Prostate Cancer
Artificial intelligence (AI) technologies are transforming the way medical images are analyzed, offering unprecedented capabilities in quantitatively extracting features that go beyond traditional visual... Read moreUltrasound
view channel
AI Identifies Heart Valve Disease from Common Imaging Test
Tricuspid regurgitation is a condition where the heart's tricuspid valve does not close completely during contraction, leading to backward blood flow, which can result in heart failure. A new artificial... Read more
Novel Imaging Method Enables Early Diagnosis and Treatment Monitoring of Type 2 Diabetes
Type 2 diabetes is recognized as an autoimmune inflammatory disease, where chronic inflammation leads to alterations in pancreatic islet microvasculature, a key factor in β-cell dysfunction.... Read moreNuclear Medicine
view channel
Novel PET Imaging Approach Offers Never-Before-Seen View of Neuroinflammation
COX-2, an enzyme that plays a key role in brain inflammation, can be significantly upregulated by inflammatory stimuli and neuroexcitation. Researchers suggest that COX-2 density in the brain could serve... Read more
Novel Radiotracer Identifies Biomarker for Triple-Negative Breast Cancer
Triple-negative breast cancer (TNBC), which represents 15-20% of all breast cancer cases, is one of the most aggressive subtypes, with a five-year survival rate of about 40%. Due to its significant heterogeneity... Read moreGeneral/Advanced Imaging
view channel
AI-Powered Imaging System Improves Lung Cancer Diagnosis
Given the need to detect lung cancer at earlier stages, there is an increasing need for a definitive diagnostic pathway for patients with suspicious pulmonary nodules. However, obtaining tissue samples... Read more
AI Model Significantly Enhances Low-Dose CT Capabilities
Lung cancer remains one of the most challenging diseases, making early diagnosis vital for effective treatment. Fortunately, advancements in artificial intelligence (AI) are revolutionizing lung cancer... Read moreImaging IT
view channel
New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible
Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Global AI in Medical Diagnostics Market to Be Driven by Demand for Image Recognition in Radiology
The global artificial intelligence (AI) in medical diagnostics market is expanding with early disease detection being one of its key applications and image recognition becoming a compelling consumer proposition... Read moreIndustry News
view channel
GE HealthCare and NVIDIA Collaboration to Reimagine Diagnostic Imaging
GE HealthCare (Chicago, IL, USA) has entered into a collaboration with NVIDIA (Santa Clara, CA, USA), expanding the existing relationship between the two companies to focus on pioneering innovation in... Read more
Patient-Specific 3D-Printed Phantoms Transform CT Imaging
New research has highlighted how anatomically precise, patient-specific 3D-printed phantoms are proving to be scalable, cost-effective, and efficient tools in the development of new CT scan algorithms... Read more
Siemens and Sectra Collaborate on Enhancing Radiology Workflows
Siemens Healthineers (Forchheim, Germany) and Sectra (Linköping, Sweden) have entered into a collaboration aimed at enhancing radiologists' diagnostic capabilities and, in turn, improving patient care... Read more