healthcare technology

“ATMAN AI”, an Artificial Intelligence algorithm that can detect the presence of COVID-19 disease in Chest X Rays, has been developed to combat COVID fatalities involving lung. ATMAN AI is used for chest X-ray screening as a triaging tool in Covid-19 diagnosis, a method for rapid identification and assessment of lung involvement. This is a joint effort of the DRDO Centre for Artificial Intelligence and Robotics (CAIR), 5C Network & HCG Academics. This will be utilized by online diagnostic startup 5C Network with support of HCG Academics across India.

Triaging COVID suspect patients using X Ray is fast, cost effective and efficient. It can be a very useful tool especially in smaller towns in India owing to lack of easy access to CT scans there.

This will also reduce the existing burden on radiologists and make CT machines which are being used for COVID be used for other diseases and illness owing to overload for CT scans.

The novel feature namely “Believable AI” along with existing ResNet models have improved the accuracy of the software and being a machine learning tool, the accuracy will improve continually.

Chest X-Rays of RT-PCR positive hospitalized patients in various stages of disease involvement were retrospectively analysed using Deep Learning & Convolutional Neural Network models by an indigenously developed deep learning application by CAIR-DRDO for COVID -19 screening using digital chest X-Rays. The algorithm showed an accuracy of 96.73%.

 read more at http://indiaai.gov.in/news/drdo-cair-5g-network-and-hcg-academics-develop-atman-ai

As researchers learn more about the brain, it has become clear that responsive neurostimulation is becoming increasingly effective at probing neural circuit function and treating neuropsychiatric disorders, such as epilepsy and Parkinson's disease. But current approaches to designing a fully implantable and biocompatible device able to make such interventions have major limitations: their resolution isn't high enough and most require large, bulky components that make implantation difficult with risk of complications.

A Columbia Engineering team led by Dion Khodagholy has come up with a new approach that shows great promise to improve such devices. Building on their earlier work to develop smaller, more efficient conformable bioelectronic transistors and materials, the researchers orchestrated their devices to create high performance implantable circuits that allow reading and manipulation of brain circuits.

Their multiplex-then-amplify (MTA) system requires only one amplifier per multiplexer, in contrast to current approaches that need an equal number of amplifiers as number of channels.

The team built the MTA device and then confirmed its functionality by developing a fully implantable, responsive embedded system that can acquire—in real time—individual neural action potentials using conformable conducting polymer-based electrodes. It can accomplish this with low-latency arbitrary waveform stimulation and local data storage—all within a miniaturized (approximately the size of a quarter) physical footprint.

Khodagholy collaborated on the study, published today by Proceedings of the National Academy of Sciences (PNAS), with Jennifer N. Gelinas, Department of Neurology and the Institute for Genomic Medicine at Columbia University Irving Medical Center.

read more at https://medicalxpress.com/news/2021-05-neuroelectronic-brain-circuits.html