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Facebook Aims to Make MRI Scans Faster Using AI With New York University

Advanced image reconstruction might enable ultra-low-dose CT scans suitable for vulnerable populations, such as pediatric patients, Facebook said

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Facebook has forged a partnership with the New York University (NYU) on a research project that aims to make magnetic resonance imaging (MRI) scans up to 10 times faster by leveraging the power of Artificial Intelligence (AI).

If the project, called fastMRI, yields the desired results, it will make MRI technology available to more people, expanding access to this key diagnostic tool, Facebook said in a blog post on Monday.

MRI scanners provide doctors and patients with images that typically show a greater level of detail related to soft tissues — such as organs and blood vessels — than is captured by other forms of medical imaging.

But they are relatively slow, taking anywhere from 15 minutes to over an hour, compared with less than a second or up to a minute, respectively, for X-ray and CT scans.

These long scan times can make MRI machines challenging for young children, as well as for people who are claustrophobic or for whom lying down is painful.

Additionally, there are MRI shortages in many rural areas and in other countries with limited access, resulting in long scheduling backlogs.

Making MRI scanners faster has several benefits, including increased access to these devices for patients.

Sufficiently accelerated MRI devices could also reduce the amount of time patients must hold their breath during imaging of the heart, liver, or other organs in the abdomen and torso.

Increased speed could let MRI machines fill the role of X-ray and CT machines for some applications, allowing patients to avoid the ionising radiation associated with those scans.

MRI Scans
Making MRI scanners faster has several benefits, including increased access to these devices for patients. Pixabay

This NYU-Facebook project will initially focus on changing how MRI machines operate.

Currently, scanners work by gathering raw numerical data in a series of sequential views and turning the data into cross-sectional images of internal body structures that doctors then use to evaluate a patient’s health.

The larger the data set to be gathered, the longer the scan will take.

Using AI, it may be possible to capture less data and therefore scan faster, while preserving or even enhancing the rich information content of magnetic resonance images.

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The key is to train artificial neural networks to recognise the underlying structure of the images in order to fill in views omitted from the accelerated scan, Facebook said.

The Facebook Artificial Intelligence Research (FAIR) group, believes that though this project will initially focus on MRI technology, its long-term impact could extend to many other medical imaging applications.

For example, the improvements afforded by AI have the potential to revolutionise CT scans as well.

Advanced image reconstruction might enable ultra-low-dose CT scans suitable for vulnerable populations, such as pediatric patients, Facebook said. (IANS)

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Facebook Seeking To Patent a Software To Build User’s Profile

Around 29 million Facebook accounts were hacked in September

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Facebook seeks to patent software to analyse who lives with you. Pixabay

Despite facing flak for leakage of personal data of millions of its users in recent times, Facebook is seeking to patent a software that could help it build profile of an user’s household – the number of people in the household, the interests that they share, nature of their relationships or even the devices that they use.

The software, which could be used to target ads, would analyse images posted to Facebook or Instagram, The Los Angeles Times reported on Friday.

An online system that predicts household features of a user — household size and demographic composition — provides improved and targeted content delivery to the user and the user’s household, according to the patent application.

To help determine whether people live in the same home, the software could look at how often people are tagged in pictures together and at the captions of the photos, it said.

“Without such knowledge of a user’s household features, most of content items that are sent to the user are poorly tailored to the user and are likely ignored,” said the patent application, which was filed last year and made public on Thursday.

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Facebook, social media. Pixabay

Facebook could also incorporate “past posts, status updates, friendships, messaging history, past tagging history” and web browsing history to put together a profile of a household or family, the report added.

The proposed online system seeks to apply one or more models trained using deep learning techniques to generate the predictions.

“For example, a trained image analysis model identifies each individual depicted in the photos of the user; a trained text analysis model derive household member relationship information from the user’s profile data and tags associated with the photos,” stated the application.

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Those profiles, in turn, could be made available to third parties that want to target “content” to users, it said.

Facebook told The Los Angeles Times that applying for the patent does not necessarily mean it will build or use the software.

Around 29 million Facebook accounts were hacked in September. (IANS)