GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK
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AI Toilet Tool Offers Remote Patient Monitoring for Gastrointestinal Health

AI Toilet Tool Offers Remote Patient Monitoring for Gastrointestinal Health | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

Researchers at Duke University are developing an artificial intelligence tool for toilets that would help providers improve care management for patients with gastrointestinal issues through remote patient monitoring.

 

The tool, which can be installed in the pipes of a toilet and analyzes stool samples, has the potential to improve treatment of chronic gastrointestinal issues like inflammatory bowel disease or irritable bowel syndrome, according to a press release.

 

When a patient flushes the toilet, the mHealth platform photographs the stool as it moves through the pipes. That data is sent to a gastroenterologist, who can analyze the data for evidence of chronic issues.

 

A study conducted by Duke University researchers found that the platform had an 85.1 percent accuracy rate on stool form classification and a 76.3 percent accuracy rate on detection of gross blood.

 

read the entire article at https://mhealthintelligence.com/news/ai-toilet-tool-offers-remote-patient-monitoring-for-gastrointestinal-health

 


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Grant awarded to develop artificial intelligence to improve stroke screening and treatment in smaller hospitals

Grant awarded to develop artificial intelligence to improve stroke screening and treatment in smaller hospitals | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

New artificial intelligence technology that uses a common CT angiography (CTA), as opposed to the more advanced imaging normally required to help identify patients who could benefit from endovascular stroke therapy (EST), is being developed at The University of Texas Health Science Center at Houston (UTHealth).

 

Two UTHealth researchers worked together to create a machine-learning artificial intelligence tool that could be used for assessing a stroke at every hospital that takes care of stroke patients - not just at large academic hospitals in major cities. 

 

Research to further develop and test the technology tool is funded through a five-year, $2.5 million grant from the National Institutes of Health (NIH). 

 

"The vast majority of stroke patients don't show up at large hospitals, but in those smaller regional facilities. And most of the emphasis on screening techniques is only focused on the technologies used in those large academic centers. With this technology, we are looking to change that," said Sunil Sheth, MD, assistant professor of neurology at McGovern Medical School at UTHealth.

 

Sheth set out with Luca Giancardo, PhD, assistant professor with the Center for Precision Health at UTHealth School of Biomedical Informatics, to develop a quicker way to assess patients. The result was a novel deep neural network architecture that leverages brain symmetry. Using CTAs, which are more widely available, the system can determine the presence or absence of a large vessel occlusion and whether the amount of "at-risk" tissue is above or below the thresholds seen in those patients who benefitted from EST in the clinical trials.

 

"This is the first time a data set is being specifically collected aiming to address the lack of quality imaging available for stroke patients at smaller hospitals," Giancardo said.

 

read the complete press release with further details on the work at https://www.uth.edu/news/story.htm?id=9fccdefb-ff91-4775-a759-a786689956ea

 


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AI can now design new antibiotics in a matter of days

AI can now design new antibiotics in a matter of days | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

Imagine you’re a scientist who needs to discover a new antibiotic to fight off a scary disease. How would you go about finding it?

 

Typically, you’d have to test lots and lots of different molecules in the lab until you find one that has the necessary bacteria-killing properties. You might find some contenders that are good at killing the bacteria only to realize that you can’t use them because they also prove toxic to humans. It’s a very long, very expensive, and probably very aggravating process.

 

But what if, instead, you could just type into your computer the properties you’re looking for and have your computer design the perfect molecule for you?

 

That’s the general approach IBM researchers are taking, using an AI system that can automatically generate the design of molecules for new antibiotics.

 

In a new paper, published in Nature Biomedical Engineering, the researchers detail how they’ve already used it to quickly design two new antimicrobial peptides — small molecules that can kill bacteria — that are effective against a bunch of different pathogens in mice.

 

Normally, this molecule discovery process would take scientists years. The AI system did it in a matter of days.

 

That’s great news, because we urgently need faster ways to create new antibiotics.

How IBM’s AI system works

IBM’s new AI system relies on something called a generative model. To understand it at its simplest level, we can break it down into three basic steps.

 

First, the researchers start with a massive database of known peptide molecules.

 

Then the AI pulls information from the database and analyzes the patterns to figure out the relationship between molecules and their properties. It might find that when a molecule has a certain structure or composition, it tends to perform a certain function.

 

This allows it to “learn” the basic rules of molecule design.

 

Finally, researchers can tell the AI exactly what properties they want a new molecule to have. They can also input constraints (for example: low toxicity, please!). Using this info on desirable and undesirable traits, the AI then designs new molecules that satisfy the parameters. The researchers can pick the best one from among them and start testing on mice in a lab.

 

The IBM researchers claim that their approach outperformed other leading methods for designing new antimicrobial peptides by 10 percent. They found that they were able to design two new antimicrobial peptides that are highly potent against diverse pathogens, including multidrug-resistant K. pneumoniae, a bacterium known for causing infections in hospital patients. Happily, the peptides had low toxicity when tested in mice, an important signal about their safety (though not everything that’s true for mice ends up being generalizable to humans).

 

read the original unedited article at  https://www.vox.com/future-perfect/22360573/ai-ibm-design-new-antibiotics-covid-19-treatments

 

read the paper by the IBM researchers - Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations


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nrip's curator insight, April 10, 2021 11:55 PM

This is an exciting paper to read. Using AI to identify brand-new types of antibiotics by training a neural network is not new and has been/is being explored in a number of labs around the world, Last year we read about the use of AI to predict which molecules will have bacteria-killing properties. Slowly but surely as more research builds upon more research in this space, we will be exploring using data driven personalized medicines which will be tailored to individuals rather than generalized on a best case fit.

 

But will a day ever come when we have medicines which have no side effects?

 

What do you think?

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Patients may not take advice from AI doctors who know their names

Patients may not take advice from AI doctors who know their names | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

As the use of artificial intelligence (AI) in health applications grows, health providers are looking for ways to improve patients' experience with their machine doctors.

 

Researchers from Penn State and University of California, Santa Barbara (UCSB) found that people may be less likely to take health advice from an AI doctor when the robot knows their name and medical history. On the other hand, patients want to be on a first-name basis with their human doctors.

 

When the AI doctor used the first name of the patients and referred to their medical history in the conversation, study participants were more likely to consider an AI health chatbot intrusive and also less likely to heed the AI's medical advice, the researchers added. However, they expected human doctors to differentiate them from other patients and were less likely to comply when a human doctor failed to remember their information.

 

The findings offer further evidence that machines walk a fine line in serving as doctors.

 

Machines do have advantages as medical providers, said Joseph B. Walther, distinguished professor in communication and the Mark and Susan Bertelsen Presidential Chair in Technology and Society at UCSB. He said that, like a family doctor who has treated a patient for a long time, computer systems could — hypothetically — know a patient’s complete medical history. In comparison, seeing a new doctor or a specialist who knows only your latest lab tests might be a more common experience, said Walther, who is also director of the Center for Information Technology and Society at UCSB.

 

“This struck us with the question: ‘Who really knows us better: a machine that can store all this information, or a human who has never met us before or hasn’t developed a relationship with us, and what do we value in a relationship with a medical expert?’” said Walther. “So this research asks, who knows us better — and who do we like more?”

 

Accepting AI doctors

As medical providers look for cost-effective ways to provide better care, AI medical services may provide one alternative. However, AI doctors must provide care and advice that patients are willing to accept, according to Cheng Chen, doctoral student in mass communications at Penn State.

 

“One of the reasons we conducted this study was that we read in the literature a lot of accounts of how people are reluctant to accept AI as a doctor,” said Chen. “They just don’t feel comfortable with the technology and they don’t feel that the AI recognizes their uniqueness as a patient. So, we thought that because machines can retain so much information about a person, they can provide individuation, and solve this uniqueness problem.”

 

The findings suggest that this strategy can backfire. “When an AI system recognizes a person’s uniqueness, it comes across as intrusive, echoing larger concerns with AI in society,” said Sundar.

 

In the future, the researchers expect more investigations into the roles that authenticity and the ability for machines to engage in back-and-forth questions may play in developing better rapport with patients.

 

read more at https://news.psu.edu/story/657391/2021/05/10/research/patients-may-not-take-advice-ai-doctors-who-know-their-names

 


Lire l'article complet sur : news.psu.edu


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AI and ML can revolutionize life sciences, and biology can move AI further ahead

AI and ML can revolutionize life sciences, and biology can move AI further ahead | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

Two scientific leaps,  in machine learning algorithms and powerful biological imaging and sequencing tools , are increasingly being combined to spur progress in understanding diseases and advance AI itself.

 

Cutting-edge, machine-learning techniques are increasingly being adapted and applied to biological data, including for COVID-19.

 

Recently, researchers reported using a new technique to figure out how genes are expressed in individual cells and how those cells interact in people who had died with Alzheimer's disease.

 

Machine-learning algorithms can also be used to compare the expression of genes in cells infected with SARS-CoV-2 to cells treated with thousands of different drugs in order to try to computationally predict drugs that might inhibit the virus.

 

While, Algorithmic results alone don't prove the drugs are potent enough to be clinically effective. But they can help identify future targets for antivirals or they could reveal a protein researchers didn't know was important for SARS-CoV-2, providing new insight on the biology of the virus

 

read the original article which speaks about a lot more at https://www.axios.com/ai-machine-learning-biology-drug-development-b51d18f1-7487-400e-8e33-e6b72bd5cfad.html

 

 


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nrip's curator insight, April 15, 2021 10:26 AM

The insight in this article is shared among a number of early adopters and tinkerers in the Healthcare ML space. A number of specific problems which are being worked on within the Machine learning space which relate to life sciences are stimulants which help us advance the science of machine learning much faster than other areas.

 

This is because the science of Biology requires more than patterns being found and re-applied to identify something. It requires understanding the interaction of all the contributing factors behind that pattern being created in the first place. So, creating a drug to target a protein involved in a disease does require understanding how the genes that give rise to that protein are regulated.

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Contactless Sleep Sensing in Nest Hub

Contactless Sleep Sensing in Nest Hub | GAFAMS, STARTUPS & INNOVATION IN HEALTHCARE by PHARMAGEEK | Scoop.it

People often turn to technology to manage their health and wellbeing, whether it is

  • to record their daily exercise,
  • measure their heart rate, or increasingly,
  • to understand their sleep patterns.

 

Sleep is foundational to a person’s everyday wellbeing and can be impacted by (and in turn, have an impact on) other aspects of one’s life — mood, energy, diet, productivity, and more.

 

As part of Google's ongoing efforts to support people’s health and happiness, Google has announced Sleep Sensing in the new Nest Hub, which uses radar-based sleep tracking in addition to an algorithm for cough and snore detection.

 

The new Nest Hub, with its underlying Sleep Sensing features, is the first step in empowering users to understand their nighttime wellness using privacy-preserving radar and audio signals.

 

Understanding Sleep Quality with Audio Sensing

The Soli-based sleep tracking algorithm gives users a convenient and reliable way to see how much sleep they are getting and when sleep disruptions occur.

 

However, to understand and improve their sleep, users also need to understand why their sleep is disrupted.

 

To assist with this, Nest Hub uses its array of sensors to track common sleep disturbances, such as light level changes or uncomfortable room temperature. In addition to these, respiratory events like coughing and snoring are also frequent sources of disturbance, but people are often unaware of these events.

 

As with other audio-processing applications like speech or music recognition, coughing and snoring exhibit distinctive temporal patterns in the audio frequency spectrum, and with sufficient data an ML model can be trained to reliably recognize these patterns while simultaneously ignoring a wide variety of background noises, from a humming fan to passing cars.

 

The model uses entirely on-device audio processing with privacy-preserving analysis, with no raw audio data sent to Google’s servers. A user can then opt to save the outputs of the processing (sound occurrences, such as the number of coughs and snore minutes) in Google Fit, in order to view personal insights and summaries of their night time wellness over time.

 

read the entire unedited blog post at https://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html

 

 


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