We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Feather Safety Razor

Download Mobile App




Highly Advanced AI Model Precisely Detects COVID-19 by Analyzing Lung Images

By HospiMedica International staff writers
Posted on 13 Aug 2021
Print article
Illustration
Illustration
Researchers have designed and validated an image-based detection of COVID-19 with the aid of artificial intelligence (AI) models by using a model to automatically collect imaging data from the lung lobes. This data was then analyzed to yield features as potential diagnostic biomarkers for COVID-19.

These diagnostic biomarkers using the AI model were subsequently used to differentiate COVID-19 patients from both pneumonia and healthy patients. The entire model was developed by researchers from the Terasaki Institute for Biomedical Innovation (TIBI; Los Angeles, CA, USA) with a cohort of 704 chest X-rays and then independently validated with 1,597 cases from multiple sources comprised of healthy, pneumonia, and COVID-19 patients. The results showed excellent performance by the model in classifying diagnoses of the various patients.

Medical imaging has long been a vital tool for the diagnosis and prognostic assessments of many diseases. In recent years, the use of AI models has been used in conjunction with this imaging to augment their diagnostic capabilities. By using these models, some features can be extracted from images that may reveal disease characteristics not identified by the naked eye. The power to process data in this intelligent manner can have a big impact on the medical field, especially with the current growth in imaging features and the need for high precision in medical decisions.

There is a huge demand for rapid and accurate detection of COVID-19 infection. The primary detection method has been using reverse transcription-polymerase chain reaction (RT-PCR) on samples collected from nasal or throat swabs. However, this method is subject to inaccuracies due to sampling errors, low viral load, and the method’s sensitivity limitations. This is an especially significant issue for patients who are in the early stages of infection. An additional diagnostic tool for COVID-19 can come from images of lungs. For diagnosing lung diseases, chest X-rays or CT scans are the primary resources, and they can be used to distinguish COVID-19 from other types of lung injuries, as well as to assess the severity of lung involvement in COVID-19. These types of images can enhance the diagnostic capabilities for COVID-19 patients, especially if they are coupled with AI models. The use of computer modeling with data extracted from medical images shows great promise in enabling precision medicine and can revolutionize medical practice in the clinic. Developing methodologies to capture entire sets of information while suppressing irrelevant features enhances the reliability of AI models. The proposed approach would be a step towards applying them in precision medicine and can provide an efficient, inexpensive, and non-invasive way to strengthen the diagnostic capabilities of imaging.

“This highly advanced artificial intelligence model further helps our ability to precisely detect COVID-19 patients. In addition, such a model can be applied for diagnosis of other diseases using different imaging modalities,” said lead researcher Samad Ahadian, Ph.D.

“Artificial intelligence-driven models with diagnostic and predictive capabilities are a powerful tool that are an important part of our research platforms here at the institute,” said Ali Khademhosseini, Ph.D., Director and CEO of TIBI. “This will carry over into countless applications in the biomedical field and in the clinic.”

Related Links:

Terasaki Institute for Biomedical Innovation


Print article

Channels

Critical Care

view channel
Image: EsoGuard has demonstrated over 90% specificity and 90% sensitivity in identifying Barrett’s Esophagus (Photo courtesy of Lucid Diagnostics)

Biomarker Based Non-Endoscopic Technology Identifies Risk for Esophageal Cancer

Barrett's esophagus (BE) is the benign and treatable precursor condition to esophageal adenocarcinomas (EAC) which is usually diagnosed at an advanced stage and is difficult to treat. Finding BE, a sign... Read more

Surgical Techniques

view channel
Image: Bio-glue enables near-instantaneous gelling, sealing and healing of injured tissue (Photo courtesy of Pexels)

Game-Changing ‘Bio-Glue’ Could End Use of Surgical Sutures and Staple

Tissue adhesive washout and detachment are major issues for medical practitioners and may prove fatal for patients, especially when the separation happens in vital organs like the lungs, liver and the heart.... Read more

Patient Care

view channel
Image: Future wearable health tech could measure gases released from skin (Photo courtesy of Pexels)

Wearable Health Tech Could Measure Gases Released From Skin to Monitor Metabolic Diseases

Most research on measuring human biomarkers, which are measures of a body’s health, rely on electrical signals to sense the chemicals excreted in sweat. But sensors that rely on perspiration often require... Read more

Health IT

view channel
Image: AI can reveal a patient`s heart health (Photo courtesy of Mayo Clinic)

AI Trained for Specific Vocal Biomarkers Could Accurately Predict Coronary Artery Disease

Earlier studies have examined the use of voice analysis for identifying voice markers associated with coronary artery disease (CAD) and heart failure. Other research groups have explored the use of similar... Read more
Copyright © 2000-2022 Globetech Media. All rights reserved.