Bangalore: DRDO Centre for Artificial Intelligence and Robotics on Friday announced that it has developed an Artificial Intelligence algorithm, "ATMAN AI" for Chest X Rays screening to detect Covid-19, in collaboration with 5C Network & HCG Academics. This new AI tool will be used by 5C Network, India's largest digital network of Radiologists, with support of HCG Academics across India. The product has been designed to reduce the burden of CT scans and make covid diagnosis accessible for smaller towns by helping in making the process of X-ray screening fast and efficient. Mr. Kalyan Sivasailam, CEO of 5C network, commented, "Utilizing the algorithms for chest X-ray is an effective triaging tool which can be accessible to the common man in remotest districts of this country. This will have a significant impact on timely care and appropriate treatment."
The Defence Research and Development Organisation (DRDO) has developed an artificial intelligence algorithm that can detect the presence of the COVID-19 virus in chest X-rays. The AI tool, ATMAN AI, was developed by DRDO's Centre for Artificial Intelligence and Robotics (CAIR), with support from 5C Network & HCG Academics. Triaging using X-ray in COVID-19 diagnosis is a method for the rapid identification and assessment of the lungs, according to a statement issued by HCG Academics. The tool will be used by 5C Network, the country's largest digital network of radiologists, with the support of HCG Academics. Triaging potential patients using X-ray is fast, cost-effective, and efficient.
Hospitals and universities across the country can now access thousands of Covid-19 images and scans in a bid to develop artificial intelligence solutions to tackle the virus. NHSX has collected more than 40,000 CT scans, MRIs and X-rays from more than 10,000 patients across 18 NHS trusts over the course of the pandemic. Together they form the National Covid-19 Chest Imaging Database (NCCID) and have been extended to hospitals and universities who are using the images to track patterns of illness. It is hoped the database will speed up diagnosis of coronavirus, ultimately leading to quicker treatment and less pressure on the NHS by predicting things like the need for additional ICU capacity. The British Society of Thoratic Imaging, Royal Surrey NHS Foundation Trust and AI company Faculty are working with NHSX on the database as part of the NHS AI Lab.
The vast majority of today's healthcare data comes from medical scans, and doctors have become stressed and overburdened as they struggle to interpret the images while managing patient care. By using AI and deep-learning technology to analyze patient scans, doctors can obtain results much faster while also improving diagnostic accuracy. Scans are not as easy to decipher as they may appear. Many contain dozens of images that doctors must pore over to arrive at a diagnosis. Pinpointing the exact location and dimensions of fractures, nodules, and other lesions is often difficult.
AI was used for the detection and quantification of COVID-19 cases from chest x-ray and CT scan images . Researchers have developed a deep learning model called COVID-19 detection neural network (COVNet), for differentiating between COVID-19 and community-acquired pneumonia based on visual 2D and 3D features extracted from volumetric chest CT scan Singh et al. developed a novel deep learning model using MultiObjective Differential Evolution and convolutional neural networks for COVID-19 diagnosis using a chest CT Unprecedented pace of efforts to address the COVID-19 pandemic situation is leveraged by big data and artificial intelligence (AI). Various offshoots of AI have been used in several disease outbreaks earlier. AI can play a vital role in the fight against COVID-19. AI is being successfully used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, diagnosis of COVID-19, disease management by resource allocation, facilitating training, record maintenance and pattern recognition for studying the disease trend.