GE’s ABUS | Philips AI Incubator | Big Mammo Week

“Breast tomosynthesis will be introduced, it’s just a question of when and to what extent.”

Lund University’s Sophia Zackrisson PhD forecasting greater adoption of DBT in breast cancer screening following her study detailing 3D mammography’s cancer detection advantages compared to traditional mammography.



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GE Updates Invenia ABUS
GE Healthcare announced the US launch of the Invenia Automated Breast Ultrasound (ABUS) 2.0, introducing new features focused on improving the exam experience (for patients and operators) and customizing exams for each patient. The 2nd generation Invenia system remains the only FDA-approved ultrasound breast screening system designed for detecting cancer in dense breast tissue, touting a 55% improved breast cancer detection rate when used in addition to mammography (vs. just mammography). GE wisely referenced the legal and procedural momentum towards informing patients about their breast density and offering supplemental screening to those with dense breasts, which indeed creates far more opportunities for the Invenia 2.0 than when its predecessor launched in 2014.


Philips’ AI Incubator
Philips is trying out the role of incubator, launching a 12-week startup collaboration program that will host 19 different healthcare AI startups (out of 750 applicants) at the company’s innovation hubs in Cambridge (US), Eindhoven (the Netherlands), Bangalore (India), and Shanghai (China). The new program focuses on the application of AI-based clinical decision support tools (e.g. image interpretation, analysis and integration, and workflow tools), giving the startups access to Philips’ know-how and ecosystem (hospital, academic, industry and financial partners), and “validating their propositions, helping to build, test and scale their ideas, and exploring possible collaborations.” That last “possible collaborations” part is likely of the greatest interest to Philips, and who can blame them given the AI leadership race that’s underway.


Breast Density DL Model Gets Clinical
A team of Boston area researchers developed a deep learning model that can match experienced mammographers’ ability to measure breast density. Here’s how: the researchers trained the DL model on BI-RADS breast density interpretations from 41,479 mammograms (27,684 women), tested it on a set of 8,677 mammograms (5,741 women) and a five-radiologist reader study on 500 mammograms, and then implemented it in clinical practice where eight radiologists reviewed 10,763 consecutive mammograms using the model. These three groups found that the model “worked remarkably well,” with radiologists agreeing with the model’s assessment in roughly 95% of all cases. Just as notable given that most AI studies are still limited to academia, the new DL model has been in use at Mass General Hospital since the start of 2018, interpreting over 16,000 scans so far this year.


A Contrast Dye Kidney Solution
Canadian researchers discovered why contrast dyes used in imaging procedures (specifically, angiography) can damage kidneys and outlined ways to make these tests safer for people at risk for acute kidney injury. The researchers used high-powered microscopes to map the progression of contrast dye through the mouse kidneys, revealing that dye flushes through fully-hydrated kidneys but is absorbed into kidneys with low hydration, which can cause inflation and lead to serious damage. As a result of this finding, researchers developed a drug that stops the kidney from absorbing the dye, potentially allowing the use of contrast dyes in at-risk patients without harming their kidneys.



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  • Conquest Imaging expanded its ultrasound parts, probes, and service businesses, shifting its probe repairs in-house and reducing its probe prices by between 20% and 50% as a result of the new capability.



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