Low cost Malaria Diagnostic imaging on your smartphone
I love to see real world problems being solved (many of you may have read my rants on startups solving 1st World Silicon Valley Hipster Problems)
Malaria is the worlds biggest killer. The tiny Mosquito has over 2000 species, attacks over half the worlds population and kills over 725, 000 people a year.
You may have recently seen Bill Gates infographic which describes the loss of human life each year to Malaria and yet it gets almost no media attention compared to Sharks, Crocodiles, Snakes and many other scary but minor killers.
Trouble is the people who get it often don’t know for a long time, except in very severe cases many of the symptoms are similar to other common illnesses such as the flu.
Existing testing procedures rely on access to a lab and blood samples. Most people who are inflected are nowhere near a lab, therefore end up being a host and carrier and mosquitos then propagate this by biting the infected person and transmitting it to others.
If you can manage to break or reduce this retransmission cycle you can decrease the overall number of people who get infected and reinfect and over time the death rate drops.
Unfortunately this is difficult in countries where sufferers are located in remote country, far from medical and laboratory facilities.
So what if your local health worker could run a screening test using a smart phone and a add on lens to quickly diagnose you in the field and commence treatment.
Code named Athelas, Tanay Tandon from Cupertino has hacked up solution over a weekend at the Y Combinator YC Hacks hackathon.
This is not Tanay’s first project, as a High School student he built a smart phone news reader called Clipped using algorithms to summarise news stories into short summaries which now has delivered 40 million summaries with 250,000 users.
So over the weekend they built a low-cost lens attachment to the smartphone camera that images blood at high magnification. The attachment magnifies/focuses on the sample by means of a 1mm ball lens.
On top of that they adapted an Open Source Computer Vision platform called OpenCV to algorithmically count and identify cells in the bloodstream to automatically diagnose disease/conditions and then store these in a new high scalability database platform called Firebase (thanks to Firebase for blogging about Athelas)
For more than 2 centuries, cell morphology – or the practice of viewing/analyzing a person’s blood in order to diagnose conditions – has been the primary way to approach medicine.
Literally every facet of the medical world relies on blood cell analysis to diagnose conditions. Malaria, Chronic Diseases, Cancers, and all sorts of Parasites are all first detected when a physician manually recognizes the given cell type in your blood sample.
Through predictive cell counting, Athelas aims to mimic the process conducted in lab-grade environments in rural areas.
Fantastic project worthy of funding.
Can someone throw $500k at this guy and help him build a product?