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GE Healthcare 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 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritisation and quality control.

Research shows that up to 25 per cent 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 worldwide, anywhere from 5-15 per cent require intensive care surveillance and intubation for ventilatory support.

“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 ventilation. 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 while proper ETT placement can be difficult. 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 analyses 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 prioritised 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, 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 analyse 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.

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