Researchers at the University of Waterloo have developed a system using harmless microwaves and artificial intelligence (AI) software to detect even small, early-stage tumours within minutes. The new inexpensive technology could save lives and money by routinely screening women for breast cancer without exposure to radiation. The research, Breast Tumor Diagnosis using Machine Learning with Microwave Probes, was presented at a recent conference in Yemen. "Our top priorities were to make this detection-based modality fast and inexpensive," said Omar Ramahi, a professor of electrical and computer engineering at Waterloo. "We have incredibly encouraging results and we believe that is because of its simplicity. " A prototype device -- the culmination of 15 years of work on the use of microwaves for tumour detection, not imaging -- cost less than $5,000 to build. It consists of a small sensor in an adjustable box about 15 centimetres square that is situated under an opening in a padded examination table. Patients lie face-down on the table so that one breast at a time is positioned in the box. The sensor emits microwaves that bounce back and are then processed by AI software on a laptop computer. By comparing the tissue composition of one breast with the other, the system is sensitive enough to detect anomalies less than one centimetre in diameter. Ramahi said a negative result could quickly rule out cancer, while a positive result would trigger referral for more expensive tests using mammography or magnetic resonance imaging (MRI). "If women were screened regularly with this, potential problems would be caught much sooner -- in the early stages of cancer," he said. "Our system can complement existing technology, reserving much more expensive options for when they're really needed. "We need a mixture, a combination of technologies. When our device sent up a red flag, it would mean more investigation was warranted. " In addition to reducing patient wait times and enabling earlier diagnosis, Ramahi said, the device would eliminate radiation exposure, improve patient comfort and work on particularly dense breasts, a problem with mammograms. It would also save health-care systems enormous amounts of money and, because of its low cost and ease of use, dramatically increase access to screening in the developing world. Researchers have applied for a patent and started a company, Wave Intelligence Inc. of Waterloo, to commercialize the system and hope to begin trials on patients within six months. Three rounds of preliminary testing included the use of artificial human torsos known as phantoms.

Researchers at the University of Waterloo have developed a system using harmless microwaves and artificial intelligence (AI) software to detect even small, early-stage tumours within minutes. The new inexpensive technology could save lives and money by routinely screening women for breast cancer without exposure to radiation. The research, Breast Tumor Diagnosis using Machine Learning with Microwave Probes, was presented at a recent conference in Yemen.

"Our top priorities were to make this detection-based modality fast and inexpensive," said Omar Ramahi, a professor of electrical and computer engineering at Waterloo. "We have incredibly encouraging results and we believe that is because of its simplicity.

" A prototype device -- the culmination of 15 years of work on the use of microwaves for tumour detection, not imaging -- cost less than $5,000 to build.

It consists of a small sensor in an adjustable box about 15 centimetres square that is situated under an opening in a padded examination table. 

Patients lie face-down on the table so that one breast at a time is positioned in the box. The sensor emits microwaves that bounce back and are then processed by AI software on a laptop computer.
By comparing the tissue composition of one breast with the other, the system is sensitive enough to detect anomalies less than one centimetre in diameter.

Ramahi said a negative result could quickly rule out cancer, while a positive result would trigger referral for more expensive tests using mammography or magnetic resonance imaging (MRI).

 "If women were screened regularly with this, potential problems would be caught much sooner -- in the early stages of cancer," he said. "Our system can complement existing technology, reserving much more expensive options for when they're really needed.

 "We need a mixture, a combination of technologies. When our device sent up a red flag, it would mean more investigation was warranted.

" In addition to reducing patient wait times and enabling earlier diagnosis, Ramahi said, the device would eliminate radiation exposure, improve patient comfort and work on particularly dense breasts, a problem with mammograms. 

It would also save health-care systems enormous amounts of money and, because of its low cost and ease of use, dramatically increase access to screening in the developing world. 

Researchers have applied for a patent and started a company, Wave Intelligence Inc. of Waterloo, to commercialize the system and hope to begin trials on patients within six months. Three rounds of preliminary testing included the use of artificial human torsos known as phantoms.

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AI Used to Measure Sugar in the Blood

Researchers hope this can someday replace the invasive finger prick. 

Anyone suffering from diabetes knows how important tracking sugar in the blood is. Technology has improved that process but it still requires needles and finger pricks.

Researchers at the University of Warwick in the UK are trying to change that, applying artificial intelligence to the problem.

In a paper published in journal Scientific, the scientists led by Dr. Leandro Pecchia demonstrated how they could detect sugar in the blood using ECG signals and off-the-shelf-wearable sensors. 

The AI system works just as well
Two pilot studies of healthy volunteers showed the system's average sensitivity and specificity was about 82% which is comparable with the current system used to detect hypoglycemia.  As it stands continuous glucose monitors or CGMs are available via the NHS for detecting sugar levels in the blood. They measure the glucose in fluid using a sensor with a needle. The senor sends alarms and data to a device. The devices often need to be calibrated two times a day and include fingerprick blood glucose level tests. 

Fingerpicks are never pleasant and in some circumstances are particularly cumbersome. Taking fingerpick during the night certainly is unpleasant, especially for patients in pediatric age," said Dr. Pecchia in a press release announcing the work. “Our innovation consisted in using artificial intelligence for automatic detecting hypoglycemia via few ECG beats. This is relevant because ECG can be detected in any circumstance, including sleeping.”

Subject's own data used to train the AI algorithm

What may have made the Warwick scientists' method so effective is that the AI algorithms are trained with the subject's own data. If cohort data was used the system would not give back the same results.

"Our approach enables personalised tuning of detection algorithms and emphasize how hypoglycaemic events affect ECG in individuals. Basing on this information, clinicians can adapt the therapy to each individual. Clearly more clinical research is required to confirm these results in wider populations. This is why we are looking for partners., Dr. Pecchia said.

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Scientists Used AI To Discover a New Powerful Antibiotic

It’s been recently published in the scientific journal Cell that a powerful new type of antibiotics has been discovered, using a pioneering machine-learning method. The work was led by biologist Jim Collins at the Massachusetts Institute of Technology in Cambridge. 

The definition of machine-learning is the application of artificial intelligence (AI) to learn from its own experience. This is the ability of computerized systems to learn and improve from ‘their own’ experience without being explicitly programmed, like playing chess against itself without human players.

This is the first time that the use of AI has led to the identification of a completely new kind of antibiotic from scratch, without the input of any previous human assumptions. The new antibiotic was named halicin. When tested on mice, it was found to be highly effective against a wide range of bacteria, including tuberculosis and strains that were considered ‘pan-resistant’ and thus untreatable.
In previous experiments involving other antibiotic compounds, resistance has typically arisen within a day or two, but in the case of halicin it did not occur even after 30 days. The name halicin was picked as a homage to HAL, the intelligent computer in the movie 2001: A Space Odyssey. 

Why Is the Discovery So Significant?
In recent years bacterial resistance to antibiotics has bee rising exponentially, while the discovery and regularity approval of new antibiotics is slowing down. Experts have made the grim prediction that by the year 2050, infections could become resistant to the point where they would kill 10 million people per year. One of the ways to avoid this terrifying scenario is by finding new antibiotics. The problem, according to Collins, is that people keep finding the same molecules again and again. That’s why the development of new modes of searching is so important. 

How Was AI Used to Discover the New Antibiotic?
The team of researchers developed an AI algorithm inspired by the brain’s structure. This system, called a neural network, learns the features and qualities of molecules atom by atom. The neural network was then trained to spot molecules which hinder the growth of the bacterium known as E. coli. 

Once the system was trained, it was used to screen about 6000 molecules. The researchers asked the model to predict which ones would be effective against E. Coli and to show only the ones that look different from conventional antibiotics.  Out of the results, about 100 candidate molecules were selected for physical testing. Luckily, one of these molecules - halicin - turned out to be an extremely potent antibiotic.  

The algorithm predicts the functions without any assumptions about how drugs work and without chemical groups being labeled, and it’s because of that neutral approach it can learn new patterns, unknown to human experts.

What Does It All Mean for The Future?

AI has already been put to use for medical research purposes, but in a slightly different way than Collins and his team have used it here. Instead of searching for specific structures and molecular classes, their network looked for molecules that display a particular activity.

The team hopes to broaden the general approach for finding new antibiotics and even use their methods to design molecules from scratch. The use of AI technology within healthcare is still considered in its infancy, but it is proving a powerful tool that may help us reach some major breakthroughs in the medical field. Many experts believe the next level of medicine and medical drugs depends enormously on our progress in computer processing power. 

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Artificial intelligence tracks down acute myeloid leukaemia (AML)

Tracking down acute myeloid leukaemia (AML), researchers have proved that artificial intelligence can detect forms of blood cancer.

The approach used by researchers revolved around the gene activity analysis of cells that are present in the blood. This approach could support conventional diagnostics and accelerate therapy of the disease.

Some studies have been carried out on this topic and the results are available. Thus, there is an enormous data pool. Researchers have collected virtually everything that is currently available said on the researcher.

'Transcriptome' which is a fingerprint of gene activity was the centre of focus of the researchers. In all cells, only certain genes are actually 'switches on' which gets reflected in their profiles of gene activity.

Such types of data that are derived from cells present in blood samples and spanning 1000s of genes were analysed in the study.

The transcriptome holds important information about the condition of cells. However, classical diagnostics is based on different data. We, therefore, wanted to find out what an analysis of the transcriptome can achieve using artificial intelligence, that is to say trainable algorithms, he said.

In the long term, we intend to apply this approach to further topics, in particular in the field of dementia, he added.

The study revolved around AML which without adequate treatment leads to death within weeks.
Researchers concluded that the method of tracking AML using AI when out into application, could support conventional diagnostics and help save costs.

In principle, a blood sample taken by the family doctor and sent to a laboratory for analysis could suffice. I guess that the cost would be less than 50 euros. However, we have not yet developed a workable test. We've only shown that the approach works in principle. So, we have laid the groundwork for developing a test, the researcher said.

Classical AML diagnostics includes a variety of methods. The researcher emphasised that the aim of the study is to provide the experts with a tool for diagnosis of the disease.

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AI helps identify new breast cancer types

Using artificial intelligence (AI), researchers have distinguished five types of breast cancer, which were earlier lumped into one. Researchers applied AI and machine learning (ML) to gene sequences and molecular data from breast tumours, to reveal crucial differences among these cancer types.

According to researchers, two of them are more likely to respond to immunotherapy, one was more likely to relapse on tamoxifen. “We are at the cusp of a revolution in healthcare as we get to grips with the possibilities AI and ML can open up,” said study leader author .

“Our study has shown that AI can recognise patterns in breast cancer that are beyond the limit of the human eye, and to point us to new avenues of treatment among those who have stopped responding to standard hormone therapies,” the researcher said.

The majority of breast cancers develop in the inner cells that line the mammary ducts and are “fed” by oestrogen or progesterone. These are classed as ‘luminal A’ tumours and often have the best cure rates.

However, patients within the group respond differently to standard treatments, like tamoxifen, or new treatments — needed if patients relapse — such as immunotherapy.

The researchers applied the AI-trained computer software to a vast array of data available on the genetics, molecular and cellular make-up of primary ‘luminal A’ breast tumours, along with data on patient survival.

Once trained, the AI was able to identify five different types of disease with particular patterns of response to treatment.

Women with a cancer type labelled ‘inflammatory’ had immune cells present in their tumours and high levels of a protein called PD-L1, suggesting they were likely to respond to immunotherapies.

Another group of patients had ‘triple negative’ tumours, which don’t respond to standard hormone treatments but various indicators suggest they might also respond to immunotherapy.

Patients with tumours that contained a specific change in chromosome 8 had worse survival than other groups when treated with tamoxifen and tended to relapse much earlier. These patients may benefit from an additional or new treatment to delay or prevent late relapse.

The markers identified in the study don’t challenge the overall classification of breast cancer, but find additional differences within the current sub-divisions of the disease, with important implications for treatment.

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Google’s DeepMind says its A.I. tech can spot acute kidney disease 48 hours before doctors spot it

Five years after Google acquired DeepMind, the health and artificial intelligence group is unveiling its biggest breakthrough yet in health care. Its technology is able to predict if a patient has potentially fatal kidney injuries 48 hours before many symptoms can be recognized by doctors.

In a paper published on Wednesday, DeepMind researchers said their algorithms correctly predicted 90 percent of acute kidney injuries that would end up requiring dialysis. The work was the result of a project with the U.S. Department of Veteran Affairs to help doctors get a head start on treatment.

“We’ve been really excited for the potential of using AI to support clinicians moving care from reactive to proactive and preventative,” said  DeepMind’s co-founder and clinical lead, in an interview.

About 2 million people die every year across the globe from acute kidney injury, according to researchers. The condition, which involves a sudden episode of kidney failure or damage, can be tricky for doctors to diagnose because there aren’t always immediate and clear symptoms. Studies have shown that catching it early can decrease the likelihood of serious injury or death.

In 2014, Google acquired DeepMind for a reported 500 million $ as it looked to expand in AI and bring in top industry experts to work on hard problems involving machine learning. As Alphabet and its various units have stepped into the health-care space in the past few years, much of the focus has been on using its technology to predict serious health outcomes before they happen. 

DeepMind’s health projects will soon be folded into Google Health. The group hasn’t said much publicly beyond its website, which says it’s studying how AI can be used to assist in “diagnosing cancer, predicting patient outcomes, preventing blindness, and much more.” Much of its team remains based in the U.K., although its health unit is expected to relocate to Google’s Silicon Valley headquarters.

Even in its early days, the company’s work in health care has been criticized for not adequately protecting user privacy. In a recent case, a patient sued Google and the University of Chicago Medical Center for not removing doctors’ notes and date stamps from personal medical records. And a U.K. government privacy watchdog said a hospital had illegally sent1.6 million records to Google DeepMind for a new health-care app.

The research on kidney injuries came from two separate joint studies with the VA and the Royal Free Hospital in London. DeepMind said it analyzed data stored electronically from more than 100 VA hospitals, reviewing information on hundreds of thousands of patients. Personal details like names and social security numbers were stripped from the data.

In addition to predicting acute kidney disease two days early, the company is also researching how to deliver these alerts in emergency situations so doctors properly recognize and act on them.

DeepMind’s King said there’s still work to be done to create a regulatory framework for bringing predictive tools to medicine and to better understand how they can be delivered in real time.

DeepMind’s breakthroughs might eventually augment the mobile app Streams, which is mostly used in the U.K. as a communications tool by doctors and nurses. It doesn’t currently use AI, but DeepMind has long stressed its vision of someday building an “an AI-powered assistant for nurses and doctors everywhere.”

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