Detailed malaria diagnostics with intelligent microscopy

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The best way to diagnose malaria remains microscopic examination of blood smears, to identify the plasmodium parasites that are responsible. This takes around 30 minutes of microscopy, done by a trained technician - skilled workers who are in short supply. This project will create an intelligent microscope that can greatly multiply the skills of a technician by scanning over the smears automatically, and allowing them to review only the suspicious blood cells on a tablet computer after the smear has been scanned. Malaria is one of the world's most prevalent infectious diseases. It affects 200 million per year, and causes around 400 thousand deaths - most of them children in ODA countries in sub-Saharan Africa. Impressive progress is being made in reducing the incidence of malaria, which makes good diagnosis of the condition ever more important; it is increasingly inaccurate to assume that every patient with a fever has malaria, and doing so will waste drugs and leave potentially life threatening fevers untreated. The key to reliable, useful diagnosis with an automated microscope lies in computer vision; simply acquiring digital images and tiling them together into a digital smear is an important first step, but robust analysis of the digital images means the technician need not sift through many images of healthy cells. Instead, they can concentrate their efforts on parts of the image where the algorithm identified suspicious features. Once trained, our algorithm will be able to identify many parasites, only asking for the technician's opinion in challenging, ambiguous cases when it could not identify objects with certainty. Fully automated counts of healthy and infected cells will then allow consistent quantification of test results, informing the clinician prescribing treatment and aiding in disease monitoring. Analysis of medical images raises fundamental issues with the standard "deep learning" approach of training a multi-layer neural network on hundreds of thousands of images. Such algorithms cannot accurately quantify their uncertainty (i.e. flag up when a diagnosis may be inaccurate), nor describe the reasoning that led to a given classification for an image. They require extremely large training datasets, which must often be labelled by hand. We will build a generative probabilistic model which, while not feasible in most applications due to the huge range of objects that might conceivably be found in a photograph, is possible in the relatively controlled imaging environment of a microscope. This will allow us to give a probabilistic verdict on each cell, and highlight cells that couldn't be reliably classified as healthy, infected, or something else. The generative model will also be able to identify features that led to a classification, for example highlighting infected cells in a large image of a smear. Both of these features will enable greater trust in the algorithm, and allow it to be used to support, rather than replace, existing clinical staff as well as collecting images that will allow us to improve the algorithm's performance. Computer vision is a powerful technique, but it requires high-resolution digital representations of blood smears in order to work. Our project therefore has a hardware component, where we will build on our earlier work with the OpenFlexure Microscope to create a slide-scanning instrument, capable of digitising blood smears in the field. This instrument will use low cost components and desktop digital manufacturing, so that it can be produced locally - freeing clinics from expensive international supply chains, and creating opportunities for local entrepreneurs that build valuable engineering and design skills. We have already trialled this approach with the first version of the microscope, which will shortly be available for purchase in Tanzania and Kenya, and we hope to achieve an even greater impact with a fully automated instrument.