Detailed malaria diagnostics with intelligent microscopy

Lead Research Organisation: University of Bath
Department Name: Physics


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.

Planned Impact

The ultimate beneficiaries of this work will be patients in sub-Saharan Africa, who will have faster, more reliable malaria diagnosis available to them and thus receive better healthcare - particularly as progress is made in lowering the incidence of the disease. This benefit will come through healthcare professionals, particularly technicians, who are able to treat and diagnose more patients thanks to assistance in blood smear analysis from the software and hardware instrument we will develop in this project. By replacing time-consuming manual microscopy with automated microscopy, and subsequent review of samples on a screen, we will not only multiply the skills of trained technicians, but make it much easier for trainee technicians to learn the features to look for when analysing a blood smear. Sharing a screen is far easier than observing the same sample through microscope eyepieces, and images displayed on-screen are much easier to compare with reference images than microscope slides viewed through eyepieces.

By working with Ifakara Health Institute, a Tanzanian research organisation, we ensure that our research can be immediately used to help certify a medical device in Tanzania; IHI have an ongoing relationship with the national standards bodies NIMR and TFDA and are familiar with the approval process. We have also carefully considered how our improved method will gain acceptance by medical technicians - by improving their existing practice rather than replacing it with a totally new method, or replacing the technician with a machine, we enhance their jobs rather than remove them. The instrument will be able to explain its results, highlighting parasites, eliminating healthy cells, and flagging areas where it cannot classify a feature. These difficult areas can be reviewed by a trained technician, ensuring the instrument does not give unreliable diagnoses and allowing us to collect useful images to improve future versions of the algorithm.

Our strategy of creating locally manufacturable instruments will also benefit local engineers, technicians and entrepreneurs. Releasing our designs open source, and making extensive use of digital manufacturing techniques such as 3D printing, enables high quality automated microscopes to be brought to market in ODA nations without relying on expensive imports, or the legal and financial complications resulting from patents. By moving as much as possible of the commercialisation process to Tanzania, we can bring a product to market faster and with less capital. We also ensure that local entrepreneurs adapt and market the instrument to meet local needs, and that there is an after-sale support network that does not rely on expensive international shipping. Through the Tech for Trade network, we will be able to quickly grow the number of countries where this technology is produced, and we have included budget for some initial work in Kenya.

We are also committed to public engagement in the UK, and our use of low cost hardware lends itself to workshops and exhibitions from school age to adults. We will attend science festivals, such as the Bath Taps into Science festival, as well as working with other groups to run workshops around our open source designs. We are also working with the Raspberry Pi foundation to create teaching materials related to computer-controlled microscopy, joining up ICT, Physics and Biology and bringing school lab technology up to date with modern developments.


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Title Microscope Folk comic 
Description As well as being exhibited at the "visions of science" exhibition, Tom Armstrong's comic book about a visit to our lab, "the microscope folk" has been distributed in print and online to many interested parties, collaborators, and colleagues. 
Type Of Art Artwork 
Year Produced 2018 
Impact This output was exhibited at an exhibition on campus, and has also provided imagery that we have used to illustrate talks - particularly to non scientific audiences, where the comic book style helps to make our content more approachable. 
Description We have developed and released a hardware and software design for a digital microscope that is capable of taking the high quality images required for malaria diagnostics. The impact of this is on healthcare (SDG3) and education (SDG4) in Tanzania, an ODA country. by sharing our designs as Open Source Hardware, we are able to spread this impact across the developing world and are actively working to do so with our partners Tech for Trade..
Exploitation Route Our hardware designs are already being reproduced by many people around the world, as documented on the Github community site. This will allow local manufacturers in ODA countries such as Tanzania, Kenya and Ghana to produce high tech equipment (SDG9) that is used for healthcare (SDG3) and education (SDG4).
Sectors Creative Economy,Digital/Communication/Information Technologies (including Software),Education,Electronics,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology

Description We have worked wth local producers STICLab in Tanzania to demonstrate that our microscope design can be locally produced to a standard that is capable of serious clinical imaging. These microscopes are now routinely produced by STICLab and they are pursuing them as a commercial project, for marketing to schools. The primary ODA country benefitting from this impact is Tanzania, though as we have shared all our software and designs as open source, we expect more countries will follow suit - particularly Ghana, where we have strong connections through the Tech for Trade network and Kumasi Hive. This work contributes strongly to SDG9, through developing the local high tech manufacturing capability at STICLab and more generally through "maker" spaces equipped with locally producible 3D printers. Our findings are aimed at addressing SDG3 (healthcare) through improving malaria diagnosis, and also SDG4 (education) by providing quality science teaching apparatus.
First Year Of Impact 2018
Sector Education,Electronics,Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology
Impact Types Societal,Economic

Description URF Enhancement Award
Amount £200,000 (GBP)
Organisation The Royal Society 
Sector Academic/University
Country United Kingdom
Start 10/2018 
End 03/2021
Description University Research Fellowship
Amount £600,000 (GBP)
Organisation The Royal Society 
Sector Academic/University
Country United Kingdom
Start 10/2018 
End 09/2023
Title OpenFlexure Microscope v5.20 
Description The OpenFlexure Microscope is a 3D printable design for a microscope, equipped with a high quality motorised stage for automatic focus and sample translation. It includes a range of options for the optics, from a very simple webcam-based design (suitable for use in schools, for example) to a lab-ready design with condenser optics on the transmission illumination, and a conventional microscope objective for high resolution imaging. As well as being inexpensive to produce, this microscope is designed parametrically, which makes it simple to customise for different use cases. By integrating all of the control electronics, including an embedded Raspberry Pi computer, into a portable, low cost, low power device, we have created a research tool that will enable long-running experiments, or multiple experiments in parallel, without the need to tie up expensive microscopy equipment that is often in high demand by multiple users. These same advantages make our tool particularly appropriate for healthcare and research scenarios in the developing world, or for use in education scenarios where budgets are limited. NB the date below relates to the most recent "release" of the project which represents a step change in performance and functionality. An earlier version of the microscope has been in circulation since 2015. 
Type Of Material Technology assay or reagent 
Year Produced 2018 
Provided To Others? Yes  
Impact Our microscope design is in use across the world, in countries including Kenya, Tanzania, Ghana, Paraguay, and Chile - mostly in research labs and schools. It has also been reproduced in research labs in the UK, Germany, the US, and more for use in research projects. 
Title Raspberry Pi camera lens shading code 
Description The Raspberry Pi camera module is a popular image sensor in many open source scientific hardware projects due to its low cost and high performance. One major limitation is in the firmware that applies a "lens shading correction" to images, which is problematic when using the camera with custom optics. We have improved an open source library, to allow the use of custom calibrations and manual gain settings with the camera. 
Type Of Material Technology assay or reagent 
Year Produced 2018 
Provided To Others? Yes  
Impact Our improvements have been acknowledged by the original authors of the libraries and will be incorporated "upstream". 
Description Exeter Festival of Physics workshop 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Public/other audiences
Results and Impact 15 school, pupils and members of the public attended a workshop where we build and then used 3D printed microscopes to view a number of samples, and explore physical computing concepts by writing an autofocus routine in Python.
Year(s) Of Engagement Activity 2018