Seems like things are moving along very nicely indeed
The invention proposes an approach utilizing novel and artificially intelligent hybrid sensor arrays with multiplexed detection capabilities for disease-specific biomarkers from the exhaled breath of a subject. The technology provides a rapid and highly accurate diagnosis in various COVID-19...
patents.google.com
The data was analyzed by Brainchip with a Spiking Neural Network, the adjacent confusion matrix shows the results on the test set. The test set included 31 samples- 21 positives and 10 negatives from 21 tested subjects. Zero out of 21 positive samples were identified correctly which represents 100% sensitivity and 4 out of 10 negative samples were identified correctly which represents 40% specificity. The overall accuracy was 80.65% The second study was performed with the multiuse NaNose sensors installed in Sniffphone device. The dataset included 165 samples taken from 141 subjects tested with Sniffphone device at Zayed Military Hospital - 65 samples from 65 COVID-19 positive subjects and 100 samples from 76 COVID-19 negative subjects (Several negative subjects were sampled two or three times). A Linear discriminative analysis was performed. The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 37 samples - 8 positive and 29 negative samples from 27 tested subjects. Seven out of eight positive samples were identified correctly which represents 87.5% sensitivity, and 25 out of the 29 negative samples were identified correctly which represents 86.2% specificity. The overall accuracy was therefore 86.5%.
The same data set was analyzed also by the SNN methodology. To make the SNN most efficient, 34 samples were discarded due to noise or improper vector dimensionality. Thus, the dataset included 131 samples taken from 126 subjects tested with Sniffphone device at Zayed Military Hospital- 62 samples from 62 COVID-19 positive subjects and 69 samples from 64 COVID-19 negative subjects (Several negative subjects were sampled two or three times). The adjacent confusion matrix shows the results on the test set that that was completely blind to the training and validation of the model. The test set included 53 samples - 20 positive and 33 negative samples from 53 tested subjects. Nineteen out of 20 positive samples were identified correctly which represents 95% sensitivity and 29 out of 33 negative samples were identified correctly which represents 87.87 % specificity. The overall accuracy was therefore 90.5%.
Two different analysis methods were applied on the dataset and both showed excellent results for the differentiation between COVID positive and COVID negative. While the multiuse sensors achieved a much better specificity (-87%) compared to the single use sensors (40%), this is more likely a result of the vast difference between the datasets: the dataset of the multiuse sensors included 165 samples from 141 subjects while the dataset of the single-use sensors included 66 samples from 45 subjects. During the Clinical study with COVID19 patients the company further improved the 4 components of the device: the mechanical design including the breath collection mechanism, the electronics, the sensors and the classifying algorithm.