Fetch.ai Launches Collective Learning AI to Assist In Diagnosing COVID-19 in X-Rays
SINGAPORE, Dec. 9, 2020 -- Fetch.ai, a Cambridge-based artificial intelligence lab building an open-access decentralized machine learning network for smart infrastructure, announced today the launch of its CoLearn network. This collective AI learning system enables multiple parties distributed anywhere across the globe to work together to train machine learning models to detect COVID-19 patients from X-ray scans.
According to Science Daily, highly characteristic chest X-rays can be used as a first-line triage process for identifying COVID-19 patients with pneumonia. It was also reported that the chest X-rays have a high positive predictive value of 83.8% for SARS-CoV-2 infection in the setting of pandemic, making X-rays a highly accurate tool in recognizing the infection.
CoLearn provides hospitals and doctors with a network in which they can use their own private data, in the form of chest X-rays, that have been labeled according to whether the patients with pneumonia have tested positive for COVID-19 serving as a rapid diagnostic tool. It can also be used to identify the severity of a patient’s condition including recognizing the need for intubation or the need for supplemental oxygen.
“Currently, our machine learning algorithm can distinguish COVID-19 patients from those with pneumonia from other causes with an accuracy of 97%,” said Jonathan Ward, CTO of Fetch.ai. “In the context of the pandemic, with wider implementation of our CoLearn network, we have the ability to rapidly and efficiently combine information from hospitals across the globe to vastly improve prognostic predictions and ensure patients are given the right care.”
On Fetch.ai’s CoLearn network, multiple participants from anywhere in the world can securely train a shared machine learning model on their private data which the network will then learn from. Utilizing blockchain technology and AI-smart learning capabilities, it supports and trains its network to learn from private data without having access to it.
“Machine learning is changing our everyday life in ways that were previously unimaginable,” continued Humayun Sheikh, CEO of Fetch.ai. “These algorithms are everywhere; in your pocket, in traffic cameras, in the websites you visit, and they all share a common goal, to build models based on a given data set to make accurate predictions. Our goal with Fetch.ai’s collective learning module is to utilize otherwise unused and unknown data to solve complex coordination tasks, and in this case, create a database that will quickly identify the existence of COVID-19 in patients while allowing hospitals without experience in ML/AI to benefit from the collective models.”
Machine learning (ML) based methods have shown unprecedented success in the reliable analysis of medical images, with a common application of ML-based image analysis being the classification of images based on shared features, according to the National Library of Medicine. Fetch.ai’s CoLearn, as of November 2020, has correctly identified COVID-19 cases from a training set of chest X-ray images submitted from hospitals and private practices using a model trained across a distributed network with nodes in Los Angeles and London. As a result, it can give doctors a digital second opinion that confirms or questions their assessment of a patient's condition.
For more information on Fetch.ai or to join its CoLearn network please visit: fetch.ai
About Fetch.ai
Fetch.ai, a Cambridge-based artificial intelligence lab, is building the infrastructure required for autonomous software agents to begin performing useful economic work on behalf of individuals, machines, businesses, and organizations. Fetch.ai’s network is based around open-source technology and gives users access to the power of AI on a world-scale secure dataset to carry out complex coordination tasks in the modern economy. For more information, please visit https://www.fetch.ai.
Source: Fetch.ai
