AI app could help diagnose HIV more accurately

Pioneering technology developed by UCL and Africa Health Research Institute (AHRI) researchers could transform the ability to accurately interpret HIV test results, particularly in lowand middle-income countries. Academics from the London Centre for Nanotechnology at UCL and AHRI used deep learning (artificial intelligence/AI) algorithms to improve health workers' ability to diagnose HIV using lateral flow tests in rural South Africa. Their findings 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 harnessing the potential of mobile phone sensors, cameras, processing power and data sharing capabilities, the team developed an 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. Lateral flow tests - or rapid diagnostic tests (RDTs) - have been used throughout the COVID-19 pandemic and play an important role in disease control and screening.