Full Body Image | Alzheimer’s Predictor | Enough Images

“While I had imagined what the images would look like for years, nothing prepared me for the incredible detail we could see on that first scan.”

UC Davis Department of Biomedical Engineering professor, Simon Cherry, on the first images produced by UC Davis’ new EXPLORER total-body PET/CT system.



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Ezra’s Prostate MRI Plan
AI startup, Ezra, raised $4 million to support the launch of a new service that intends to replace painful prostate cancer biopsies with MRI scans. Although funding announcements for medical imaging AI startups is a weekly occurrence these days, Ezra’s business is unique, as it adopts some of the biggest trends seen in consumer industries (a subscription-based model, direct-to-consumer channel mechanics), and provides an imaging-based alternative for an important, but uncomfortable, screening process. Here’s how it works: At-risk men pay a $999 annual subscription fee that gets them a radiologist-reviewed MRI scan each year, plus access to medical staff and educational guides, and ongoing support if the MRI reveals signs of cancer. Ezra is doing this with a 50% gross margin using actual human radiologists, but will rely on its FDA-pending AI software (90% accurate) to automate the prostate cancer diagnosis process in the future, which will play a key role in making this $999 annual rate possible and allowing the company to scale (including scaling to other cancer types).


Full Body Image
After 13 years of planning and development, the first human images from UC Davis’ EXPLORER total-body PET/CT system debuted at RSNA this week, and early reports reveal an “astonishing” level of detail. These first images mark a key milestone for the EXPLORER system, which will be followed by its installation at UC Davis by mid-2019 and its eventual commercial launch by development partner United Imaging Healthcare of Shanghai. EXPLORER reportedly “captures radiation far more efficiently than other scanners,” allowing it to produce an image in as little as one second (40x faster than current PET scans) with a far lower dose (40x lower radiation). The ability to view what is happening in all the organs and tissues of the body simultaneously is pretty amazing, and could support a range of new diagnostic and research applications, making the EXPLORER an interesting system to watch.


Washington U’s Alzheimer’s Predictor
Researchers at Washington University developed a way to predict if patients will develop Alzheimer’s disease with MRI scans using diffusion tensor imaging (DTI). The researchers used DTI to access data on 61 patients’ white matter integrity, specifically studying fractional anisotropy (FA), which measures how well water molecules travel along the brain’s white matter tracts. As you may expect, the team found “quantifiable differences” in patients who later developed Alzheimer’s Disease (lower FA). It’s worth noting that this is the second MRI-based Alzheimer’s prediction breakthrough to come out of Washington University Mallinckrodt Institute of Radiology in as many months.


Google Absorbs DeepMind Health
Google announced plans to absorb Alphabet’s DeepMind Health business, as Google reorganizes and ramps-up its overall healthcare efforts. The star of the integration is DeepMind’s Stream mobile app, an AI-based clinical assistant that DeepMind developed for the UK NHS, and is about to be scaled to a much wider audience. The integration got the press’ controversy-watchers excited, as it gives Google access to a new level of personal information from largely unknowing patients. However, the integration is more notable for its role in Google’s growing focus on healthcare, coming as part of new Google Health CEO David Feinberg’s decision to centralize all of Google’s healthcare efforts — apparently including Alphabet subsidiaries like DeepMind. The integration also represents a more-forceful move from Google to control and then commercialize the output of its DeepMind subsidiary (at least the healthcare side of it), as a key part of its 2014 acquisition was an agreement that DeepMind would remain autonomous.


Enough Images
Research from Stanford University found that convolutional neural networks (CNNs) can accurately classify chest X-rays with a relatively modest number of labeled images (95% accuracy with 20k images), but a significant increase in labeled images only yielded “marginal” improvements (96% with 200k images). Before we get carried away and try to go too far below 20,000, the researchers’ CNN test with just 2,000 images resulted in a much lower 84% accuracy rate. The researchers suggested that this is good news for healthcare providers who would benefit from trained CNNs but don’t have access to hundreds of thousands of images for training, citing previous research that found CNNs plateau after approximately 60,000 images.



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