In total, we’ve done 974,283 classifications. Which means ONE MILLION isn’t too far away.
Until now we have simply sent all the images of each M. tuberculosis sample growing on each and every of the 14 antibiotics out to be classified by the citizen scientists. A while ago we realised that some images are “easy” in the sense that all the volunteers we show it to all give exactly the same answer. So, with a bit of work behind the scenes, we’ve written some computer code that can detect where the wells are in each image and then measure the amount of growth in each well. Now, it is isn’t perfect; it can be confused by small amounts of growth and artefacts like air bubbles and shadows, but it does mean we can confidently filter out the relatively easy images, thereby allowing us to only send the more challenging cases to you, our volunteers.
It has been a while since I updated everyone on how BashTheBug is going.
Check out the work of my friend Lucy Turner who has created some textile designs based on various tuberculosis objects (if you look carefully you can see some based on the 96-well plates we classify).
Pause for a moment and think about the 750,000+ classifications achieved by BashTheBug in its first year of existence: that is an enormous number. The next thought is usually who are the 10,000+ volunteers, the Citizen Scientists who contribute their time and energy to the project? We have not yet met any of you – until recently that is. Let me introduce you to Samir, who we met at a public engagement event.
BashTheBug has just got back from the recent European Congress of Clinical Microbiology and Infectious Diseases in Madrid, Spain. You can see some descriptive analysis of the first-dataset on this poster (free to access).
BashTheBug was officially launched one year ago today. Since then 10,213 people from all over the world have classified 735,070 images of M. tuberculosis growth, which is one every 43 seconds all year.
You’ve finished three datasets; an initial validation set from seven clinical laboratories from four continents and then two further datasets from two different Asian countries with a high burden of TB. (Well, we are 1,206 classifications short of the 121,305 we need to finish the second country, but you will probably finish that sometime tomorrow!)
Here’s to our second year and our first results, which we will share with you soon.