COVID-Net: Final Reflections

  1. March 29, 2020: COVID-Net: larger dataset, new models, and COVID-RiskNet
  2. Apr 19, 2020: COVID-Net: More data, CT Scans and Risk Stratification
  3. May 21, 2020: COVID-Net Update: More data, New models
  4. May 25, 2020: Pandemics don’t wait: How we built COVID-Net in under 7 days
  5. Jul 9, 2020: COVIDNET-CT: a new detection model for CT scans
  6. Sept 21, 2020: COVIDNet-S: A neural network for grading COVID Severity
  7. Jan 26, 2021: COVID-NET CT-2: An Evolutionary Leap
  8. July, 2021: COVID-Net Clinical ICU: Predicting ICU Admission for COVID-19 Patients
  9. Aug, 2021: COVID-Net US: A Highly Tailored Network for Ultrasound COVID-19 Screening
  1. NVidia: DarwinAI Achieves 96% Screening Accuracy for COVID-19 with Diverse CT Dataset
  2. ARM: Embedded AI for Healthcare: How We Built COVID-Net for Embedded Devices
  3. Intel: DarwinAI’s Deep Learning AI Screen Tool Helps Detect COVID-19
  4. HPE: Transparent, Dynamic, and Democratic AI
  1. ) The COVIDx dataset — a large and diverse corpus of CXR images used to train the AI. At present, the repository comprises 16,560 images from 15,528 patients across 51 countries, making it the one of the largest datasets in open access form.
  2. The COVID-Net model — the deep neural network responsible for case detection. The model currently differentiates between positive COVID-19 infections, pneumonia infections and normal patients. As of this writing, the model has an accuracy of 93.3% with a sensitivity of 91.0% and a positive predictive value of 98.9%.
  3. The COVID-Net S model — a deep neural network responsible for scoring the lung disease severity for COVID-positive patients. The predicted scores can be used by clinicians and healthcare workers to obtain a better understanding of the patient’s disease stage and progression, which can then be used for individualized patient care decisions and treatment planning (e.g., oxygen therapy, ventilator use).
  4. The COVID-NET User Interface — a report generation and case management interface that allows medical professionals to query, view and analyze patient scans in a streamlined fashion.
  • Creating new predictive analyses (top)
  • Finding and examining previously completed analyses
  • Examining radiological images corresponding to an analysis using the COVID-Net UI radiology viewer (bottom)
  • Generating a PDF report of a predictive analysis

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CEO, DarwinAI

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Sheldon Fernandez

Sheldon Fernandez

CEO, DarwinAI

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