Unlike pathogenic laboratory testing which produces a significant amount of false-negatives while still being a time-consuming method, Machine Learning models  can display radio-logical and image patterns via CT chest scans. 3D CT volume can detect the virus's transmission by using a lung-region 3D CT scan which is segmented and fed into a 3D deep neural network to predict the infection rate.

University of California, Santa Cruz

Machine Learning models can be used to better understand the proteins and complexes that are incvolved with the novel Coronavirus. This includes producing predictions of pandemic regarding hospitalizations, patient outcomes, and simulations.

Machine Learning Analysis 

Sensitivity and specificity of CT scans varies with COVID-19, therefore cannot be the determinant factor in diagnosing infection. Produce a medical image using multiple X-ray measurements from different angles of the patient's chest.

CT Scans

Allam, M., Cai, S., Ganesh, S., Venkatesan, M., Doodhwala, S., Song, Z., . . . Coskun, A. F. (2020). COVID-19 Diagnostics, Tools, and Prevention. Diagnostics, 10(6), 409. doi:10.3390/diagnostics10060409

https://www.mdpi.com/2075-4418/10/6/409

COVID-19 Diagnostics, Tools, and Prevention

Machine Learning models can be used to understand the protein complexes involved with infection and what treatments/drugs produce a given relationship.

Protein structure prediction

Large-Scale Analysis of CT Scans: 

Machine Learning models can provide healthcare systems with a forecast of patient outcomes, risk of hospitalization, and probability of complications ranging from respiratory distress to organ failure. The model takes in blood samples and CT scan datasets to develop machine learning models of high risk patients. 

At high speed, these tools (logistic and FbProphet models) also observe trends in the accumulated epidemiological data with which they predict how the future epidemic curve will look like. It's determined that by end of October, approximately 14.1 million people will be infected, in a cumulative aspect. 


Pandemic Predicitions

Pandemic Simulations

- Chest CT Imaging Features of COVID-19 Patients from Study #1

CT Imaging Features of COVID-19 Patients

Coronavirus pneumonia tends to affect the entire lung as opposed to other viral pneumonia affecting small parts. As per respiratory clinicians and radiologists, the 3 features  of coronavirus pneumonia are ground-glass opacities, consolidation, and pleural effusion seen in the chest of the CT scans.

COVID-19 pneumonia vs. other viral pneumonia

CNN is a strong utility that is used for classifying images. Built on layers organized by three dimensions. The neurons of a specific layer do not attach to a full set of neurons to the later layer. The output is a diminished vector of probability scores to produce a model of COVID-19 classification.

Convolutional neural networks

- 103 patients with lung CTs were chosen for the study
- The CT scans from 107 healthy patients were taken (control group)
- Figures show the relationship between the volume of blood vessels and its cross-sectional area, where the area of a circle is measured by taking a slice of the blood vessel
- The volume of blood vessels in healthy patients was lower compared to COVID-19 patients
- However, there is a slight increase in blood vessel volume in healthy patients when the cross section area of the blood vessels is between ~1.25 and 3.5 mm^2
- The increase in blood vessel volume in COVID-19 patients could be due to inflammation in the blood vessels to increase oxygen uptake by the lungs


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