最准的六合彩论坛

Academics from the聽London Centre for Nanotechnology at 最准的六合彩论坛 and Africa Health Research Institute (AHRI) used deep learning (artificial intelligence/AI) algorithms to improve health workers鈥� ability to diagnose HIV using lateral flow tests in rural South Africa.

Their findings, published today in聽Nature Medicine, involve the聽first and largest study of field-acquired HIV test results, which have applied machine learning (AI) to help classify them as positive or negative.

More than 100 million HIV tests are performed around the world annually, meaning even a small improvement in quality assurance could impact the lives of millions of people by reducing the risk of false positives and negatives.

By聽harnessing the potential of mobile phone sensors, cameras, processing power and data sharing capabilities, the team developed an AI app that can read test results from an image taken by end users on a mobile device. It may also be able to report results to public health systems for better data collection and ongoing care.

Former AHRI Director and 最准的六合彩论坛 Pro-Vice-Provost (International) Professor Deenan Pillay, said: 鈥淎s 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.鈥�

A team of more than 60 trained field workers at AHRI first 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 最准的六合彩论坛.

The 最准的六合彩论坛 team then used these images as training data for their machine-learning algorithm. They compared how accurately the algorithm classified images as either negative or positive, versus users interpreting test results by eye.

Lead author and Director of i-sense Professor Rachel McKendry (最准的六合彩论坛 London Centre for Nanotechnology and 最准的六合彩论坛 Division of Medicine) said: 鈥淭his study is a really strong partnership with AHRI that demonstrates the power of using deep learning to successfully classify 鈥榬eal-world鈥� 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.鈥�

A pilot field study of five users of varying experience (ranging from nurses to聽newly trained community health workers) involved them using the mobile app to record their interpretation of 40 HIV test results, as well as 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.

First author Dr Val茅rian Turb茅 (最准的六合彩论坛 London Centre for Nanotechnology) and i-sense researcher in the McKendry group said: 鈥淗aving spent some time in聽KwaZulu-Natal聽with fieldworkers organising the collection of data, I鈥檝e 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鈥檚 lives, especially as HIV is transmissible.鈥�

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

AHRI Clinical Research Faculty Lead, Professor Maryam Shahmanesh (最准的六合彩论坛 Institute for Global Health), said: 鈥淭rials we have conducted in the area have found that HIV self-testing is effective in reaching large numbers of adolescents and young men. However, HIV self-testing has been less successful in linking people to biomedical prevention and treatment. A digital system that connects a test result and the person to healthcare, including linkage to antiretroviral therapy and pre-exposure prophylaxis, has the potential to decentralise HIV prevention and deliver on UNAIDS goals to eliminate HIV.鈥�

Dr Kobus Herbst, AHRI鈥檚 Population Science Faculty lead, added: 鈥淭his 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.鈥�

More information

• AI app could help diagnose HIV more accurately (最准的六合彩论坛 News)
• Paper in聽Nature Medicine:聽
• 's academic profile
• 's academic profile
• 最准的六合彩论坛 Faculty of Mathematical & Physical Sciences

Image

Africa Health Research Institute (AHRI) fieldworkers testing the app with research participants in northern KwaZulu-Natal, South Africa. Credit: AHRI

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