Kick off Meeting (April 15, 2021) Assessing Trustworthy AI. Best Practice: Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients. In cooperation with Department of Information Engineering and Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health – University of Brescia, Brescia, Italy

On April 15, 2021 we had a real great kick off meeting for this use case:

Assessing Trustworthy AI. Best Practice: Deep Learning for predicting a multi-regional score conveying the degree of lung compromise in COVID-19 patients.

71 experts from all over the world attended.

Worldwide, the saturation of healthcare facilities, due to the high contagiousness of Sars-Cov-2 virus and the significant rate of respiratory complications is indeed one among the most critical aspects of the ongoing COVID-19 pandemic
The team of Alberto Signoroni and colleagues implemented an end-to-end deep learning architecture, designed for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients.

We will work with Alberto Signoroni and his team and apply our Z-inspection® process to assess the ethical, technical and legal implications of using Deep Learning in this context.

For more information: http://z-inspection.org/best-practice-deep-learning-for-predicting-a-multi-regional-score-conveying-the-degree-of-lung-compromise-in-covid-19-patients/