rapid Detection, containment and treatment technology

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Technology led solutions

real time detection using CT-scan and machine learning

The following project uses the Keras deep learning library to automatically analyze medical images for coronavirus testing also visual imaging diagonsis such as CT Scans and MRI Scans provide a much richer tool for tracking the effectiveness of different treatments, environmental factors which affect the spread and seriousness of the illness.

This initial project will focus on CT-Scan as tool for diagnosing Covid 19, Yan Li1 and Liming Xia1 “found that chest CT had a low rate of missed diagnosis of COVID-19 (3.9%, 2/51) and may be useful as a standard method for the rapid diagnosis of COVID-19 to optimize the management of patients. However, CT is still limited for identifying specific viruses and distinguishing between viruses. The authors suggest that the “Ground-glass opacities” G.G.O. yes you read that right, the Covid 19 cause damage to the lungs that appears similar in appearance to ground glass in the lungs. It is from these shadows on the scan that the extent of the damage can be tracked. Therefore scheduling multiple scans on each patient across large populations which when that data is shared could be used to cross correlate to see which factors and treatments affect the medical outcomes of those infected as well as how to lower infection rates.

All the information on the site is provided opensource and for free…. much of it has been gathered from the work of others. It is very much a work in progress and I am doing my best to provide links to the research I have been basing this project on. My hope is to:

  1. Share research about the use of CT scans in rapid diagonise

2. Share data, information, on the spread of the desease

3. Share knowledge on best practice techiques to analyse the Coronavirus and track its progress within individuals, groups and wide population.

4. Provide information about which machine learning approaches & technique can be used.

5. Provide practical methods A:B testing of treatments which can be deployed and successful techniques shared and techniques are standardised.

6. Track the effectiveness of treatment using deep learning to perform medical image analysis, specifically, how to apply similar techniques that have been verified to 95% Malaria and 97% Breast Cancer diagnosis therefore this project is hoping to use the Keras deep learning library to automatically analyze medical images for coronavirus testing.

benefits CT-Scans Coronavirus

  • they can provide real time information
  • machine learning can pick up the characteristic ground-glass-opacities g.g.o.
  • the CT-scan process can be designed to minimize heath worker and patient interactions
  • 95% accurate in diagonising coronavirus
  • provides more details of the actual extent of damage from virus
  • these could be used to support triage decisions and likely effectiveness of treatment options such as ventilation (eg AB test)
  • treatment effectiveness can be monitored in real time against non treated patients or against other treatment
  • information can be shared in real time to provide data driven best practice
  • the following project is based on a similar project by Dr adrian rosebrock on cancer patients
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