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 hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

GE Healthcare

GE Healthcare provides medical imaging and information technologies, medical diagnostics, patient monitoring systems,... read more Featured Products: More products

Download Mobile App





GE Healthcare Launches First X-Ray AI Algorithm to Help Assess Endotracheal Tube Placement for COVID-19 Patients

By HospiMedica International staff writers
Posted on 24 Nov 2020
Print article
Illustration
Illustration
GE Healthcare (Chicago, IL, USA) has launched a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients.

The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.02, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization and quality control. Research shows that up to 25% of patients intubated outside of the operating room have misplaced ETTs on chest X-rays, which can lead to severe complications for patients, including hyperinflation, pneumothorax, cardiac arrest and death. Moreover, as COVID-19 cases climb, anywhere from 5-15% require intensive care surveillance and intubation for ventilatory support.

Up to 45% of ICU patients, including severe COVID-19 cases, receive ETT intubation for ventilation. While proper ETT placement can be difficult, Critical Care Suite 2.0 uses AI to automatically detect ETTs in chest X-ray images and provides an accurate and automated measurement of ETT positioning to clinicians within seconds of image acquisition, right on the monitor of the x-ray system. In 94% of cases, the ET Tube tip-to-Carina distance calculation is accurate to within 1.0 cm. With these measurements, clinicians can determine if the ETT is placed correctly or if additional attention is required for proper placement. The AI generated measurements - along with an image overlay - are then made accessible in a picture archiving and communication systems (PACS).

Improper positioning of the ETT during intubation can lead to various complications, including a pneumothorax, a type of collapsed lung. While the chest X-ray images of a suspected pneumothorax patient are often marked “STAT,” they can sit waiting for up to eight hours for a radiologist’s review. However, when a patient is scanned on a device with Critical Care Suite 2.0, the system automatically analyzes images and sends an alert for cases with a suspected pneumothorax - along with the original chest X-ray - to the radiologist for review via PACS. The technologist also receives a subsequent on-device notification to provide awareness of the prioritized cases. To make the AI suite more accessible, Critical Care Suite 2.0 is embedded on a mobile X-ray device - offering hospitals an opportunity to try AI without making investments into additional IT infrastructure, security assessments or cybersecurity precautions for routing images offsite.

“The pandemic has proven what we already knew - that data, AI and connectivity are central to helping those on the front lines deliver intelligently efficient care,” said Jan Makela, President and CEO, Imaging at GE Healthcare. “GE Healthcare is not only providing new tools to help hospital staff keep up with demand without compromising diagnostic precision, but also leading the way on COVID-era advancements that will have a long-lasting impact on the industry, long after the pandemic ends.”

“In several COVID-19 patient cases, the pneumothorax AI algorithm has proved prophetic - accurately identifying pneumothoraces/barotrauma in intubated COVID-19 patients, flagging them to radiologist and radiology residents, and enabling expedited patient treatment,” said Dr. Amit Gupta, Modality Director of Diagnostic Radiography at University Hospital Cleveland Medical Center and Assistant Professor of Radiology at Case Western Reserve University, Cleveland. “Altogether, this technology is a game changer, helping us operate more efficiently as a practice, without compromising diagnostic precision. We soon will evaluate the new ETT placement AI algorithm, which we hope will be an equally valuable tool as we continue caring for critically ill COVID-19 patients.”


Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
New
Gold Member
X-Ray QA Meter
T3 AD Pro
New
LED Surgical Light
Convelar 1670 LED+/1675 LED+/1677 LED+
New
Computed Tomography System
Aquilion ONE / INSIGHT Edition

Print article

Channels

Surgical Techniques

view channel
Image: Schematic diagram of intra-articular pressure detection using a sensory system in a sheep model (Photo courtesy of Science China Press)

Novel Sensory System Enables Real-Time Intra-Articular Pressure Monitoring

Knee replacement surgery is a widely performed procedure to relieve knee pain and restore joint function, with over one million surgeries conducted annually. However, 10%-20% of patients remain dissatisfied... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.