Public Health - Santé Publique

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making.

 

We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables.

 

Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours.

 

The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.

 

 

In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

 

read the open article at https://www.nature.com/articles/s41746-021-00453-0

 

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“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

 

 

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