- Critical Care Suite 2.0 helps clinicians assess Endotracheal
Tube (ETT) placement for intubated patients, including critical
COVID-19 patients
- New AI suite includes algorithms that help radiologists
prioritize critical cases and automate processes to help cut
average review time from up to eight hours1
GE Healthcare today announced 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 percent3,4,5,6,7 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, with more than 50 million
confirmed worldwide8, anywhere from 5-15 percent require intensive
care surveillance and intubation for ventilatory support9.
“Today, clinicians are overwhelmed, experiencing mounting
pressure as a result of an ever-increasing number of patients,”
said Jan Makela, President and CEO, Imaging at GE Healthcare. “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. 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.”
Up to 45% of ICU patients, including severe COVID-19 cases,
receive ETT intubation for ventilation10,11,12. 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 cm13. 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 review14. However, when a
patient is scanned on a device with Critical Care Suite 2.0,15 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 notification16 to provide awareness
of the prioritized cases.
“Seconds and minutes matter when dealing with a collapsed lung
or assessing endotracheal tube positioning in a critically ill
patient,” explains 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. “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. 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.”
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.
Furthermore, the on-device AI offers several benefits to
radiologists and technologists:
- ETT positioning and critical findings: GE Healthcare’s
algorithms are a fast and reliable way to ensure AI results are
generated within seconds of image acquisition, without any
dependency on connectivity or transfer speeds to produce the AI
results.
- Eliminating processing delays: Results are then sent to
the radiologist while the device sends the original diagnostic
image, ensuring no additional processing delay.
- Ensuring quality: The AI suite also includes several
quality-focused AI algorithms to analyze and flag protocol and
field of view errors as well as auto rotate the images on-device.
By automatically running these quality checks on-device, it
integrates them into the technologist’s standard workflow and
enables technologist actions – such as rejections or reprocessing –
to occur at the patient’s bedside and before the images are sent to
PACS.
GE Healthcare and UC San Francisco co-developed Critical Care
Suite 2.0 using GE Healthcare’s Edison platform, which helps deploy
AI algorithms quickly and securely. Critical Care Suite 2.0 is
available on the company’s AMX 240 mobile x-ray system.
For more information on GE Healthcare and Critical Care Suite
2.0 visit the company’s virtual RSNA booth or gehealthcare.com.
Clinicians can also test the Critical Care Suite 2.0 algorithms by
uploading their own chest x-ray images to gexray.ai.
About GE Healthcare:
GE Healthcare is the $16.7 billion healthcare business of GE
(NYSE: GE). As a leading global medical technology and digital
solutions innovator, GE Healthcare enables clinicians to make
faster, more informed decisions through intelligent devices, data
analytics, applications and services, supported by its Edison
intelligence platform. With over 100 years of healthcare industry
experience and around 50,000 employees globally, the company
operates at the center of an ecosystem working toward precision
health, digitizing healthcare, helping drive productivity and
improve outcomes for patients, providers, health systems and
researchers around the world.
Follow us on Facebook, LinkedIn, Twitter, Instagram and Insights
for the latest news, or visit our website www.gehealthcare.com for
more information.
1 Rachh, Pratik et al. “Reducing STAT Portable Chest Radiograph
Turnaround Times: A Pilot Study.” Current Problems in Diagnostic
Radiology Vol. 47, No. 3 (n.d.): 156–60.
https://www.sciencedirect.com/science/article/abs/pii/S0363018817300312?via=ihub.
2 Critical Care Suite 2.0 is only available in the United States.
Not cleared or approved by the FDA. Distributed in accordance with
FDA imaging guidance regarding COVID-19 public health emergency. 3
Jemmett ME, Kendal KM, Fourre MW, Burton JH. Unrecognized
misplacement of endotracheal tubes in a mixed urban to rural
emergency medical services setting. Acad Emerg Med 2003;10:961–5. 4
Katz SH, Falk JL. Misplaced endotracheal tubes by paramedics in an
urban emergency medical services system. Ann Emerg Med
2001;37:32–7. 5 Lotano R, Gerber D, Aseron C, Santarelli R, Pratter
M. Utility of postintubation chest radiographs in the intensive
care unit. Crit Care 2000;4:50–3. 6 McGillicuddy DC, Babineau MR,
Fisher J, Ban K, Sanchez LD. 7 Is a postintubation chest radiograph
necessary in the emergency department? Int J Emerg Med
2009;2:247–9. 8 WHO Coronavirus Disease (COVID-19) Dashboard.
Published June 17, 2020. Retrieved November 10, 2020, from
https://covid19.who.int/. 9 M�hlenkamp S, Thiele H. “Ventilation of
COVID-19 patients in intensive care units.” Nature Public Health
Emergency Collection. 2020 Apr 20 :1–3 10 Hannah Wunsch, Jason
Wagner, Maximilian Herlim, David Chong, Andrew Kramer, and Scott D.
Halpern. ICU Occupancy and mechanical ventilator use in the United
States. Crit Care Med. 2013 Dec; 41(12):
10.1097/CCM.0b013e318298a139. 11 Dawei Wang, Bo Hu, Chang Hu, et
al. Clinical Characteristics of 138 Hospitalized Patients With 2019
Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA.
2020;323(11):1061-1069. doi:10.1001/jama.2020.1585 12 Lingzhong
Meng, M.D.; Haibo Qiu, M.D.; Li Wan, M.D.; Yuhang Ai, M.D.;
Zhanggang Xue, M.D.; et al. Intubation and Ventilation amid the
COVID-19 Outbreak: Wuhan’s Experience. Anesthesiology 6 2020,
Vol.132, 1317-1332. 13 GE Healthcare data on file. 14 Rachh, Pratik
et al. “Reducing STAT Portable Chest Radiograph Turnaround Times: A
Pilot Study.” Current Problems in Diagnostic Radiology Vol. 47, No.
3 (n.d.): 156–60.
https://www.sciencedirect.com/science/article/abs/pii/S0363018817300312?via=ihub.
15 Algorithm also available with GE Healthcare’s Critical Care
Suite 16 The technologist on-device notification is generated after
a delay, post exam closure, and it does not provide any diagnostic
information, nor is it intended to inform any clinical decision,
prioritization, or action.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20201123005895/en/
Kate Rodgers Katlynn.rodgers@ge.com +1 262 202-5430
Grafico Azioni General Electric (NYSE:GE)
Storico
Da Feb 2024 a Mar 2024
Grafico Azioni General Electric (NYSE:GE)
Storico
Da Mar 2023 a Mar 2024