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"Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society." VOA

Using Artificial Intelligence (AI) with thyroid ultrasound offers a quick and non-invasive approach to thyroid cancer screening, says a new study.

The study, published in the journal PLOS Pathogens, suggests that automated machine learning shows promise as an additional diagnostic tool that could improve the efficiency of thyroid cancer diagnosis.


“Machine learning is a low-cost and efficient tool that could help physicians arrive at a quicker decision as to how to approach an indeterminate nodule,” said the study’s lead author John Eisenbrey from Thomas Jefferson University in the US.

According to the researchers, at present ultrasounds can tell if a nodule looks suspicious, and then the decision is made whether to do a needle biopsy, but fine-needle biopsies only act as a peephole, they don’t reveal the whole picture.

As a result, some biopsies return inconclusive results as to whether the nodule is malignant, or cancerous in other words.

In order to improve the predictive power of the first-line diagnostic, the ultrasound, researchers looked into machine learning or AI models developed by Google.

They applied a machine learning algorithm to ultrasound images of patients’ thyroid nodules to see if it could pick out distinguishing patterns.

The researchers trained the algorithm on images from 121 patients who underwent ultrasound-guided fine needle-biopsy with subsequent molecular testing.


Cancer Ribbon. Pixabay

From 134 total lesions, 43 nodules were classified as high risk and 91 were classified as low risk, based on a panel of genes used in the molecular testing.

A preliminary set of images with known risk classifications was used to train the model or algorithm.

From this bank of labeled images, the algorithm utilised machine learning technology to pick out patterns associated with high and low risk nodules.

It used these patterns to form its own set of internal parameters that could be used to sort future sets of images; it essentially ‘trained’ itself on this new task.

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Then the investigators tested the trained model on a different set of unlabeled images to see how closely it could classify high and low genetic risk nodules, compared to molecular tests results.

The researchers found that their algorithm performed with 97 per cent specificity and 90 per cent predictive positive value, meaning that 97 per cent of patients who truly have benign nodules will have their ultrasound read as ‘benign’ by the algorithm, and 90 per cent of malignant or ‘positive’ nodules are truly positive as classified by the algorithm.

The overall accuracy of the algorithm was 77.4 per cent. (IANS)


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When you're pregnant, the immune system is seeing the placenta for the first time in decades.

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Researchers at the University of California-San Francisco found that the recently discovered subset of cells known as extrathymic Aire-expressing cells in the immune system may prevent the mother's immune system from attacking the placenta and fetus.

The researchers showed that pregnant mice who did not have this subset of cells were twice as likely to miscarry, and in many of these pregnancies fetal growth was severely restricted.

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"When you're pregnant, the immune system is seeing the placenta for the first time in decades -- not since the mother made a placenta when she herself was a fetus," said Eva Gillis-Buck, from UCSF.

"Our research suggests that this subset of immune cells is carrying out a sort of 'secondary education' -- sometimes many years after the better-known population of the educator cells have carried out the primary education in the thymus -- teaching T cells not to attack the fetus, the placenta and other tissues involved in pregnancy," she added. The findings are published in the journal Science Immunology.

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It's not surprising that over half of those surveyed feel more comfortable using emojis than talking on the phone or in person.

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Adobe's global emoji study found that emoji even helps people overcome language barriers and form connections that would otherwise be difficult to do.

"We were surprised and delighted by the discoveries made in the survey, most notably how enthusiastic respondents were for emoji as a means to express themselves," the company said in a statement.

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Emojis sometimes get criticized for being overly saccharine, but this sweetness is key when it comes to diffusing some of the heaviness of online communication.

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Jeff Bezos at the ENCORE awards.

Following the grand Richard Branson show where he carried Andhra Pradesh-born Sirisha Bandla and fellow space travelers on his shoulders after successfully flying to the edge of space, it is time for Amazon Founder Jeff Bezos to applaud Sanjal Gavande, one of the key engineers who designed the New Shephard rocket set to take Bezos and the crew to space on July 20.

Billionaire Bezos is set to fly to the edge of space aboard what is touted as the world's first unpiloted suborbital flight. Born in Kalyan, Maharashtra, Gavande is a systems engineer at Blue Origin who always dreamt of designing aerospace rockets.

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After completing Bachelor's in mechanical engineering from the University of Mumbai, she flew to the US in 2011 to pursue a Master's in mechanical engineering from the Michigan Technological University. She also applied for an engineering job at the US space agency NASA but finally landed her dream job at Blue Origin

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