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New AI mobile app could help improve HIV diagnosis. Researchers from University College London (UCL) and Africa Health Research Institute (AHRI) have developed an app that can read HIV test results from an image taken by end users on a mobile device. The app may also be able to report results to public health systems, improving data collection and ongoing care in the future.

The study set out to investigate whether an AI app could support HIV testing decisions made by fieldworkers, nurses, and community health workers, using lateral flow tests in rural South Africa. The research has been published in Nature Medicine.

Using an AI algorithm to diagnose HIV

Firstly, a team of over 60 trained field workers at AHRI helped build a library of more than 11,000 images of HIV tests taken in various conditions in the field in KwaZulu-Natal, South Africa, using a mobile health tool and image capture protocol developed by UCL.

The images were then used by UCL as training data for their machine-learning algorithm. They compared how accurately the algorithm classified images as either negative or positive, as opposed to users interpreting test results by eye.

Lead author Professor Rachel McKendry, of UCL London Centre for Nanotechnology and UCL Division of Medicine, said: “This study is a really strong partnership with AHRI that demonstrates the power of using deep learning to successfully classify ‘real-world’ field-acquired rapid test images, and reduce the number of errors that may happen when reading test results by eye. This research shows the positive impact the mobile health tools can have in low- and middle-income countries and paves the way for a larger study in the future.”

In a pilot field study of five users of varying experience, ranging from nurses to newly trained community health workers, participants used a mobile app to record their interpretation of 40 HIV test results, and to capture a picture of the tests to automatically be read by the machine learning classifier. All participants were able to use the app without training.

Reduction in errors

The machine learning classifier was able to reduce errors in reading RDTs, correctly classifying RDT images with 98.9% accuracy overall, compared to traditional interpretation of the tests by eye (92.1%).

A previous study of users of varying experience in interpreting HIV RDTs showed the accuracy varied between 80% and 97%. Other diseases that RDTs could support include malaria, syphilis, tuberculosis, influenza, and non-communicable diseases.

First author Dr Valérian Turbé, of UCL London Centre for Nanotechnology, said: “Having spent some time in KwaZulu-Natal with fieldworkers organising the collection of data, I’ve seen how difficult it is for people to access basic healthcare services. If these tools can help train people to interpret the images, you can make a big difference in detecting very early-stage HIV, meaning better access to healthcare, or avoiding an incorrect diagnosis. This could have massive implications on people’s lives, especially as HIV is transmissible.”

Researchers now plan to conduct a larger evaluation study to assess the performance of the system, with users of differing ages, gender, and levels of digital literacy.

A digital system has also been designed to connect to laboratory and healthcare management systems, where RDT deployment and supply can be better monitored and managed.

Dr Kobus Herbst, AHRI’s Population Science Faculty lead, added: “This study shows how machine learning approaches can benefit from large and diverse datasets available from the global South, but at the same time be responsive to local health priorities and needs.”

The researchers also suggest that real-time reporting of RDT results through a connected device could help in workforce training and outbreak management, for example by highlighting ‘hotspots’ where positive test numbers are high. They are currently extending the approach to other infections including COVID-19, and non-communicable disease.

Former AHRI Director Professor Deenan Pillay, from UCL Infection & Immunity, said: “As digital health research moves into the mainstream, there remain serious concerns that those populations most at need around the world will not benefit as much as those in high income settings. Our work demonstrates how, with appropriate partnerships and engagement, we can demonstrate utility and benefit for those in low- and middle-income settings.”

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