Scientist to Use AI For Newborns Diagnostics

The main goal of the study is to create an algorithm that detects deviations in the development of limb movements of newborns in the first few months
12 July 2018   1526

A team of scientists from the University of Southern California and the University of Madrid used AI to detect abnormalities in the development of newborns. The algorithm classifies the movements of the limbs and according to these data creates a forecast is for 1-12 months. This is reported by Venture Beat.

Scientists used the data of the laboratory for monitoring neuromotorics of newborns, located at the University of Southern California. Accelerometers, gyroscopes and magnetometers were attached to the feet of children. The algorithm collected data from the sensors for the left and right legs, then calculated the duration of the movements, the average and maximum acceleration, and other indicators.

Then the developers manually entered the age of the child, a scaled development score and information about it (typical or atypical), collected the predictive model. After using binary classification algorithms, taking into account the 3 best results for minimizing errors.

Based on the obtained data, artificial intelligence predicted delays in development for the first six months with an accuracy of 83.9%. For a period of 6-12 months, the accuracy was slightly lower - 77%. Detailed text and results are published in the article.

[S]tudies have demonstrated that kinematic variables, such as kicking frequency, spatiotemporal organization, and interjoint and interlimb coordination, are different between infants with typical development … and infants at risk … including infants with intellectual disability, myelomeningocele, Down syndrome, as well as infants born preterm.


The main goal of the study is to create an algorithm that detects deviations in the development of limb movements of newborns in the first few months. This will allow to take purposeful actions. Studies have shown that between children with normal development and children in the risk group, there are kinematic differences. The latter include the frequency of movement of the legs, spatial orientation and coordination of the limbs.

BNC to Monitor BTC Community's Mood

The system called Twitter Sentiment analyzes over 34M BTC-related Twitter posts each week, using AI to track the mood of the community
22 January 2020   479

Blockchain-based New Zealand-based research firm Brave New Coin (BNC) has unveiled a new system for measuring the mood of the Bitcoin community based on Twitter messages.

According to BNC, the new Twitter Sentiment rating system analyzes over 34 million BTC-related Twitter posts each week. The company uses artificial intelligence (AI) algorithms that look for records containing the words bitcoin, $ BTC and BTC and others.

BNC notes that user sentiment continues to be a “significant” factor in the price and dynamics of digital assets, and a new technique has been developed to track these sentiments. According to the BNC, it took 18 months to launch the Bitcoin Twitter Sentiment. The data obtained is divided into seven categories - Opinion, Technical Information, Inside the Network, Advertising, Bots, Macros and Hacking.

For the week ending January 17, the most common entries were in the Opinion category - their number was 30.42% of all data received. In second place was the category Technical Information, and in third inside the network (includes information on mining and hashrate).

BNC spokeswoman Pierre Ansaldi said that during the first quarter of this year, the company will also launch community sentiment analysis tools for other crypto assets.