COVID-NET CT-2: An Evolutionary Leap
Dear Colleagues,
It’s been roughly four months since the last update to our COVID-Net initiative.
Today, we are extremely proud to announce an evolutionary leap to the effort by way of COVID-Net CT-2.
COVID-Net CT-2 was built using a number of large and diverse datasets that we painstakingly created over several months with researchers from the University of Waterloo. The largest of the datasets consists of a multinational cohort of over 4500 patients across 15 countries (with over 200,000 CT slices) and has been fully curated and standardized. To our knowledge, this makes it the most sizably diverse dataset of its kind to be made publicly available.
By building COVID-NET CT-2 from such rich and voluminous data, we’ve been able to achieve a new level of screening accuracy, with COVID-19 sensitivity and positive predictive value exceeding 96% across a wide and diverse number of scenarios.
Per our previous blog post, our XAI platform was instrumental in the construction of the original COVID-Net model. For this version, we have taken the concept further by engaging two senior radiologists in Canada to validate the way in which COVID-Net CT-2 makes its decisions. Much to our delight, both confirmed the decision-making process of COVID-Net CT-2 is consistent with their own expert interpretations (details in the academic paper linked below).
In addition to illustrating the emerging cooperation between our respective domains, their validation exemplifies the importance of our XAI technology in constructing transparent and trustworthy AI.
Our academic study on this work is available here. In addition, the models and associated datasets for COVID-Net CT-2 can be accessed at our COVID-Net Central Hub.
Finally, if you’re a researcher or clinician and would like access to our explainability platform to assist with this project and gain transparency or have data to share, please email us at info@darwinai.ca.
In Solidarity,
The DarwinAI team
DarwinAI, the explainable AI company, enables enterprises to build AI they can trust. DarwinAI’s solutions have been leveraged in a variety of enterprise contexts, including in advanced manufacturing and industrial automation. Within healthcare, DarwinAI’s technology resulted in the development of Covid-Net, an open source system to diagnose Covid-19 via chest x-rays.
To learn more, visit darwinai.com or follow @DarwinAI on Twitter.
If you liked this blog post, click the 👏 below so other people will see this on Medium. For more insights from our team, follow our publication and @DarwinAI. (Plus, subscribe to our letters if you’d like to hear from us!)
