AI Detects COVID-19 in Lung Ultrasound Images
By MedImaging International staff writers Posted on 21 Mar 2024 |

During the onset of the pandemic, utilizing artificial intelligence (AI) to identify signs of COVID-19 in lung ultrasound images proved challenging due to limited patient data and a nascent understanding of the disease's manifestations. Though computational tools were applied to help detect COVID-19 from these images, the risk of misdiagnosis was high without adequately training and validating the AI to recognize features specific to COVID-19 in the lungs. Now, new research has led to the development of an AI tool that is capable of identifying COVID-19 in lung ultrasound images, similar to how facial recognition technology identifies faces in a crowd.
The AI tool developed by researchers at Johns Hopkins (Baltimore, MD, USA) is a deep neural network, a type of AI designed to mimic the interconnected neurons that enable the brain to recognize patterns, understand speech, and complete complex tasks. The AI tool employs algorithms to scan through lung ultrasound images for spotting features known as B-lines. These features are visible as bright, vertical abnormalities and are indicators of inflammation in patients with pulmonary complications. By learning from a combination of real and simulated data, it detects abnormalities in ultrasound scans that indicate a person has contracted COVID-19.
The findings of the research significantly enhance AI's role in medical diagnostics, helping healthcare professionals to promptly diagnose COVID-19 and other lung diseases. In addition to providing clinicians with the tool to rapidly assess the overwhelming number of patients in emergency rooms during a pandemic, the tool also paves the way for the development of wearables to monitor illnesses such as congestive heart failure, which can result in fluid overload in patients’ lungs, similar to COVID-19.
“We developed this automated detection tool to help doctors in emergency settings with high caseloads of patients who need to be diagnosed quickly and accurately, such as in the earlier stages of the pandemic,” said Muyinatu Bell, the John C. Malone Associate Professor of Electrical and Computer Engineering, Biomedical Engineering, and Computer Science at Johns Hopkins University. “Potentially, we want to have wireless devices that patients can use at home to monitor progression of COVID-19, too.”
“Early in the pandemic, we didn’t have enough ultrasound images of COVID-19 patients to develop and test our algorithms, and as a result our deep neural networks never reached peak performance,” said Lingyi Zhao, who developed the software while a postdoctoral fellow in Bell’s lab. “Now, we are proving that with computer-generated datasets we still can achieve a high degree of accuracy in evaluating and detecting these COVID-19 features.”
Related Links:
Johns Hopkins
Latest Ultrasound News
- Artificial Intelligence Detects Undiagnosed Liver Disease from Echocardiograms
- Ultrasound Imaging Non-Invasively Tracks Tumor Response to Radiation and Immunotherapy
- AI Improves Detection of Congenital Heart Defects on Routine Prenatal Ultrasounds
- AI Diagnoses Lung Diseases from Ultrasound Videos with 96.57% Accuracy
- New Contrast Agent for Ultrasound Imaging Ensures Affordable and Safer Medical Diagnostics
- Ultrasound-Directed Microbubbles Boost Immune Response Against Tumors
- POC Ultrasound Enhances Early Pregnancy Care and Cuts Emergency Visits
- AI-Based Models Outperform Human Experts at Identifying Ovarian Cancer in Ultrasound Images
- Automated Breast Ultrasound Provides Alternative to Mammography in Low-Resource Settings
- Transparent Ultrasound Transducer for Photoacoustic and Ultrasound Endoscopy to Improve Diagnostic Accuracy
- Wearable Ultrasound Patch Enables Continuous Blood Pressure Monitoring
- AI Image-Recognition Program Reads Echocardiograms Faster, Cuts Results Wait Time
- Ultrasound Device Non-Invasively Improves Blood Circulation in Lower Limbs
- Wearable Ultrasound Device Provides Long-Term, Wireless Muscle Monitoring
- Ultrasound Can Identify Sources of Brain-Related Issues and Disorders Before Treatment
- New Guideline on Handling Endobronchial Ultrasound Transbronchial Needle Samples
Channels
Radiography
view channel
AI-Powered Mammography Screening Boosts Cancer Detection in Single-Reader Settings
A new study has revealed that an artificial intelligence (AI)-powered solution significantly improves cancer detection in single-reader mammography settings without increasing recall rates, offering a... Read more
Photon Counting Detectors Promise Fast Color X-Ray Images
For many years, healthcare professionals have depended on traditional 2D X-rays to diagnose common bone fractures, though small fractures or soft tissue damage, such as cancers, can often be missed.... Read moreMRI
view channel
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 more
First-Of-Its-Kind AI-Driven Brain Imaging Platform to Better Guide Stroke Treatment Options
Each year, approximately 800,000 people in the U.S. experience strokes, with marginalized and minoritized groups being disproportionately affected. Strokes vary in terms of size and location within the... Read moreNuclear Medicine
view channel
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 more
Innovative PET Imaging Technique to Help Diagnose Neurodegeneration
Neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS) and Alzheimer’s disease, are often diagnosed only after physical symptoms appear, by which time treatment may no longer be effective.... Read moreGeneral/Advanced Imaging
view channel
AI Reduces CT Lung Cancer Screening Workload by Almost 80%
Lung cancer impacts over 48,000 individuals in the UK annually, and early detection is key to improving survival rates. The UK Lung Cancer Screening (UKLS) trial has already shown that low-dose CT (LDCT)... Read more
Cutting-Edge Technology Combines Light and Sound for Real-Time Stroke Monitoring
Stroke is the second leading cause of death globally, claiming millions of lives each year. Ischemic stroke, in particular, occurs when a blood vessel that supplies blood to the brain becomes blocked.... Read more
AI System Detects Subtle Changes in Series of Medical Images Over Time
Traditional approaches for analyzing longitudinal image datasets typically require significant customization and extensive pre-processing. For instance, in studies of the brain, researchers often begin... Read more
New CT Scan Technique to Improve Prognosis and Treatments for Head and Neck Cancers
Cancers of the mouth, nose, and throat are becoming increasingly common in the U.S., particularly among younger individuals. Approximately 60,000 new cases are diagnosed annually, with 20% of these cases... 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
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