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
GLOBETECH PUBLISHING LLC

Download Mobile App




Chest X-ray AI Identifies Improper Breathing Tube Placement

By MedImaging International staff writers
Posted on 31 Jan 2022
Print article
Image: AI software identifies correct placement of breathing tubes (Photo courtesy of Qure.ai)
Image: AI software identifies correct placement of breathing tubes (Photo courtesy of Qure.ai)
An artificial intelligence (AI) algorithm improves critical care management by assessing endotracheal and tracheostomy breathing tube (BT) placement.

The Qure.ai (Mumbai, India) qXR-BT standalone image analysis software is designed to analyze and determine the position of tip of a BT relative to the carina by generating a secondary digital chest X-ray image. It then automates measurements and provides the attending physician with a report on the tube’s positional accuracy in less than one minute. This enables clinicians to identify correct positioning and determine if extra attention is required. The algorithm is vendor-agnostic, and works on both portable and stationary X-ray machines.

The chest X-rays are sent to qXR-BT by means of transmission functions within the user’s picture archiving and communication system (PACS). Upon completion of processing, qXR-BT returns results to the user’s PACS or other user specified radiology software system or database in a PDF output that contains preview images that show segmented structures, outlined with a textual report describing the structures detected. The text report is restricted to the presence or absence of the breathing tubes and the carina as detected by the software.

In addition, qXR-BT outputs a digital imaging and communications in medicine (DICOM) report, which consists of a single complete additional DICOM series for each input scan containing labeled overlays that indicate the location and extent of the segmentable structures, suitable for viewing in the PACS or radiology viewer. qXR-BT uses pre-trained convolutional neural networks (CNNs) to process the images.

“qXR-BT is expected to become a standard feature of any critical care framework, giving residents and junior clinicians more confidence in reliably measuring breathing tube placement in intubated patients,” said Prashant Warier, CEO and Founder of Qure.ai. “Especially in the wake of the COVID-19 pandemic and the need for mechanical ventilation in affected patients, the need for prompt assistance to an overburdened healthcare workforce is paramount.”

Studies have shown that up to 25% of patients intubated outside of the operating room (OR) have misplaced endotracheal tubes, which can lead to severe complications such as hyperinflation, pneumothorax, cardiac arrest, and death. Moreover, up to 45% of ICU patients, including 5-15% of COVID-19 patients, require intensive care surveillance and intubation for ventilatory support.

Related Links:
Qure.ai

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
Color Doppler Ultrasound System
DRE Crystal 4PX
New
X-Ray QA Meter
Piranha CT
New
Brachytherapy Planning System
Oncentra Brachy

Print article
Radcal

Channels

MRI

view channel
Image: 11.7 teslas (T) of magnetic field vs. 1.5 and 3 T for conventional MRI machines in hospitals (Photo courtesy of CEA)

World’s Most Powerful MRI Machine Images Living Brain with Unrivaled Clarity

The world's most powerful magnetic resonance imaging (MRI) scanner has generated its first images of the human brain, demonstrating new precision levels that could shed more light on the mysterious human... Read more

Nuclear Medicine

view channel
Image: The radiotheranostic platform employs a MUC16-targeting humanized antibody, huAR9.6 (Photo courtesy of MSK)

New Radiotheranostic System Detects and Treats Ovarian Cancer Noninvasively

Ovarian cancer is the most lethal gynecological cancer, with less than a 30% five-year survival rate for those diagnosed in late stages. Despite surgery and platinum-based chemotherapy being the standard... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

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
Copyright © 2000-2024 Globetech Media. All rights reserved.