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AI can better predict biological age via smartphone data

A state-of-the-art 'Convolution Neural Network' was used to unravel the most biologically relevant motion patterns and establish their relation to general health and recorded lifespan

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There are many good uses of AI, but it can be misused too.
AI can now help astronomers find life on other planet. Pixabay
  • Artificial intelligence can now predict accurate biological age
  • It can do so by looking at a users’ smartphone data
  • This can help both technologically and medically

Artificial Intelligence (AI) technology can produce improved digital biomarkers of ageing and frailty via gathering physical activity data from smartphones and other wearables, a new study suggests.

AI has the potential to increase India's annual growth.
This technology can prove to be revolutionary. Pixabay

According to the researchers from the longevity biotech company GERO and Moscow Institute of Physics and Technology (MIPT), AI is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition and other fields.

“Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records,” said co-author Peter Fedichev, Science Director at GERO.

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“Inspired by these examples, we explored AI potential for ‘Health Risks Assessment’ based on human physical activity,” Fedichev added. For the study, published in the journal Scientific Reports, researchers analysed physical activity records and clinical data from a large 2003-2006 US National Health and Nutrition Examination Survey (NHANES).

They trained neural network to predict biological age and mortality risk of the participants from one week long stream of activity measurements. A state-of-the-art ‘Convolution Neural Network’ was used to unravel the most biologically relevant motion patterns and establish their relation to general health and recorded lifespan.

This technology can be used to create anti-ageing procedures. Wikimedia Commons

“We report that AI can be used to further refine the risks models,” Fedichev said. “Combination of ageing theory with the most powerful modern machine learning tools will produce even better health risks models to mitigate longevity risks in insurance, help in pension planning, and contribute to upcoming clinical trials and future deployment of anti-ageing therapies,” Fedichev noted. IANS

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Google Claims Eye Doctors Can Turn More Effective Using AI

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm. 

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The research team at Google AI believes that some of these pitfalls may be avoided if the computer can "explain" its predictions. Pixabay

As Artificial Intelligence (AI) continues to evolve, diagnosing diseases has become faster with greater accuracy. A new study from the Google AI research group shows that physicians and algorithms working together are more effective than either one alone.

In the study, to be published in the journal Ophthalmology, the researchers created a system which not only improved the ophthalmologists’ diagnostic accuracy but also improved the algorithm’s accuracy.

The study expands on previous work from Google AI showing that its algorithm works roughly as well as human experts in screening patients for a common diabetic eye disease called diabetic retinopathy.

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To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance. Pixabay

“What we found is that AI can do more than simply automate eye screening, it can assist physicians in more accurately diagnosing diabetic retinopathy. AI and physicians working together can be more accurate than either one alone,” said lead researcher Rory Sayres.

Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said.

According to the team, previous attempts to use computer-assisted diagnosis shows that some screeners rely on the machine too much, which leads to repeating the machine’s errors, or under-rely on it and ignore accurate predictions.

The research team at Google AI believes that some of these pitfalls may be avoided if the computer can “explain” its predictions.

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Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said. Pixabay

To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance.

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Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm.

With assistance, general ophthalmologists match but do not exceed the model’s accuracy, while retina specialists start to exceed the model’s performance. (IANS)