Artificial intelligence to now help diagnose breast cancer

Agencies
August 10, 2019

Washington D.C., Aug 10: Researchers discovered an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. 

The new system, described in a study published in the journal 'JAMA Network Open,' helped interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurate or better as experienced pathologists.

"It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA.

A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year.

That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).

"Medical images of breast biopsies contain a great deal of complex data and interpreting them can be very subjective. Distinguishing breast atypia from ductal carcinoma in situ is important clinically but very challenging for pathologists. Sometimes, doctors do not even agree with their previous diagnosis when they are shown the same case a year later," said Elmore, who is also a researcher at the UCLA Jonsson Comprehensive Cancer Center.

The scientists reasoned that artificial intelligence could provide more accurate readings consistently because by drawing from a large data set, the system can recognise patterns in the samples that are associated with cancer but are difficult for humans to see.

The team fed 240 breast biopsy images into a computer, training it to recognise patterns associated with several types of breast lesions, ranging from benign (noncancerous) and atypia to ductal carcinoma in situ, or DCIS, and invasive breast cancer. Separately, the correct diagnoses for each image were determined by a consensus among three expert pathologists.

To test the system, the researchers compared its readings to independent diagnoses made by 87 practising U.S. pathologists. While the artificial intelligence program came close to performing as well as human doctors in differentiating cancer from non-cancer cases, the AI program outperformed doctors when differentiating DCIS from atypia, considered the greatest challenge in breast cancer diagnosis.

The system correctly determined whether scans showed DCIS or atypia more often than the doctors; it had sensitivity between 0.88 and 0.89, while the pathologists' average sensitivity was 0.70. (A higher sensitivity score indicates a greater likelihood that diagnosis and classification are correct.)

"These results are very encouraging. There is low accuracy among practising pathologists in the U.S. when it comes to the diagnosis of atypia and ductal carcinoma in situ, and the computer-based automated approach shows great promise," Elmore said.

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Agencies
May 20,2020

Washington D.C., May 20: While a dairy-rich diet is helpful in meeting the body's calcium requirement, outcomes of a large international study links eating at least two daily servings of dairy with lower risks of diabetes and high blood pressure.

The dairy-rich diet also proved to lower the cluster of factors that heighten cardiovascular disease risk (metabolic syndrome). The study was published online in journal BMJ Open Diabetes Research & Care.

The observed associations were strongest for full-fat dairy products, the findings indicated.

Previously published research has suggested that higher dairy intake is associated with a lower risk of diabetes, high blood pressure, and metabolic syndrome. But these studies have tended to focus on North America and Europe to the exclusion of other regions of the world.

To see whether these associations might also be found in a broader range of countries, the researchers drew on people taking part in the Prospective Urban Rural Epidemiology (PURE) study.

Participants were all aged between 35 and 70 and came from 21 countries: Argentina; Bangladesh; Brazil; Canada; Chile; China; Colombia; India; Iran; Malaysia; Palestine; Pakistan; Philippines, Poland; South Africa; Saudi Arabia; Sweden; Tanzania; Turkey; United Arab Emirates; and Zimbabwe.

Usual dietary intake over the previous 12 months was assessed by means of Food Frequency Questionnaires. Dairy products included milk, yogurt, yogurt drinks, cheese and dishes prepared with dairy products, and were classified as full or low fat (1-2 percent).

Butter and cream were assessed separately as these are not commonly eaten in some of the countries studied.

Information on personal medical history, use of prescription medicines, educational attainment, smoking and measurements of weight, height, waist circumference, blood pressure and fasting blood glucose were also collected.

Data on all five components of the metabolic syndrome were available for nearly 113,000 people: blood pressure above 130/85 mm Hg; waist circumference above 80 cm; low levels of (beneficial) high-density cholesterol (less than 1-1.3 mmol/l); blood fats (triglycerides) of more than 1.7 mmol/dl; and fasting blood glucose of 5.5 mmol/l or more.

Average daily total dairy consumption was 179 g, with full-fat accounting for around double the amount of low fat: 124.5+ vs 65 g.

Some 46, 667 people had metabolic syndrome--defined as having at least 3 of the 5 components.

Total dairy and full-fat dairy, but not low-fat dairy, was associated with a lower prevalence of most components of metabolic syndrome, with the size of the association greatest in those countries with normally low dairy intakes.

At least 2 servings a day of total dairy were associated with a 24 percent lower risk of metabolic syndrome, rising to 28 percent for full-fat dairy alone, compared with no daily dairy intake.

The health of nearly 190,000 participants was tracked for an average of nine years, during which time 13,640 people developed high blood pressure and 5351 developed diabetes.

At least 2 servings a day of total dairy was associated with a 11-12 percent lower risk of both conditions, rising to a 13-14 percent lower risk for 3 daily servings. The associations were stronger for full fat than they were for low-fat dairy.

This is an observational study, and as such can't establish the cause. Food frequency questionnaires are also subject to recall, and changes in metabolic syndrome weren't measured over time, all of which may have influenced the findings.

Nevertheless, the researchers suggest: "If our findings are confirmed in sufficiently large and long term trials, then increasing dairy consumption may represent a feasible and low-cost approach to reducing [metabolic syndrome], hypertension, diabetes, and ultimately cardiovascular disease events worldwide."

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News Network
February 26,2020

New York, Feb 26:  A new wearable sensor that works in conjunction with artificial intelligence (AI) technology could help doctors remotely detect critical changes in heart failure patients days before a health crisis occurs, says a study.

The researchers said the system could eventually help avert up to one in three heart failure readmissions in the weeks following initial discharge from the hospital and help patients sustain a better quality of life.

"This study shows that we can accurately predict the likelihood of hospitalisation for heart failure deterioration well before doctors and patients know that something is wrong," says the study's lead author Josef Stehlik from University of Utah in the US.

"Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalisation and stave off worsening heart failure," Stehlik added.

According to the researchers, even if patients survive, they have poor functional capacity, poor exercise tolerance and low quality of life after hospitalisations.

"This patch, this new diagnostic tool, could potentially help us prevent hospitalizations and decline in patient status," Stehlik said.

For the findings, published in the journal Circulation: Heart Failure, the researchers followed 100 heart failure patients, average age 68, who were diagnosed and treated at four veterans administration (VA) hospitals in Utah, Texas, California, and Florida.

After discharge, participants wore an adhesive sensor patch on their chests 24 hours a day for up to three months.

The sensor monitored continuous electrocardiogram (ECG) and motion of each subject.

This information was transmitted from the sensor via Bluetooth to a smartphone and then passed on to an analytics platform, developed by PhysIQ, on a secure server, which derived heart rate, heart rhythm, respiratory rate, walking, sleep, body posture and other normal activities.

Using artificial intelligence, the analytics established a normal baseline for each patient. When the data deviated from normal, the platform generated an indication that the patient's heart failure was getting worse.

Overall, the system accurately predicted the impending need for hospitalization more than 80 per cent of the time.

On average, this prediction occurred 10.4 days before a readmission took place (median 6.5 days), the study said.

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Agencies
June 10,2020

Early treatment with the antiviral drug remdesivir has been found to reduce viral load and prevent lung disease in macaques infected with SARS-CoV-2 that causes COVID-19, according to a study.

The findings, published in the journal Nature on Tuesday, support the early use of remdesivir treatment in patients with COVID-19 to prevent progression to pneumonia.

Researchers from the National Institutes of Health in the US noted that remdesivir has broad antiviral activity and has been shown to be effective against infections with SARS-CoV and MERS-CoV in animal models.

The drug is being tested in human clinical trials for the treatment of COVID-19, they said.

Researcher Emmie de Wit and colleagues investigated the effects of remdesivir treatment in rhesus macaques, a recently established model of SARS-CoV-2 infection.

Two sets of six macaques were inoculated with SARS-CoV-2.

One group was treated with remdesivir 12 hours later -- close to the peak of virus reproduction in the lungs -- and these macaques received treatment every 24 hours until six days after inoculation.

In contrast to the control group, the researchers found that macaques that received remdesivir did not show signs of respiratory disease, and had reduced damage to the lungs.

Viral loads in the lower respiratory tract were also reduced in the treated animals; viral levels were around 100 times lower in the lower-respiratory tract of remdesivir-treated macaques 12 hours after the first dose, they said.

The researchers said that infectious virus could no longer be detected in the treatment group three days after initial infection, but was still detectable in four out of six control animals.

Despite this virus reduction in the lower respiratory tract, no reduction in virus shedding was observed, which indicates that clinical improvement may not equate to a lack of infectiousness, they said.

Dosing of remdesivir in the rhesus macaques is equivalent to that used in humans, the researchers noted.

They cautioned that it is difficult to directly translate the timing of treatment used in corresponding disease stages in humans, because rhesus macaques normally develop only mild disease.

However, researchers said the results indicate that remdesivir treatment of COVID-19 should be initiated as early as possible to achieve the maximum treatment effect.

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