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AI tool that measures heart rate and blood pressure via camera to be trialled by polyclinic patients

AI tool that measures heart rate and blood pressure via camera to be trialled by polyclinic patients

Straits Times20-06-2025

By analysing a 30-second video of a user's face, the tool is able to detect levels of blood flow in specific facial regions such as the forehead, cheeks, and nose. PHOTO: SINGHEALTH POLYCLINICS
AI tool that measures heart rate and blood pressure via camera to be trialled by polyclinic patients
SINGAPORE - An artificial intelligence (AI) tool that allows people to measure their heart rate and blood pressure with just a phone camera will be tested by a group of polyclinic patients in the coming months.
DeepHealthVision , which SingHealth Polyclinics (SHP) and South Korean digital health company injewelme are collaborating on, was announced during the first day of the 10th Asia Pacific Primary Care Research Conference 2025 on June 20 .
The tool, which is able to detect blood flow changes in facial regions such as the forehead and cheeks, can produce vital sign readings without the need for external medical devices such as blood pressure monitors. Apart from phones, patients can also use the cameras in other devices such as laptops and tablets.
This would make healthcare monitoring more accessible and convenient by eliminating the need for expensive medical devices and frequent clinic visits, said Clinical Associate Professor Tan Ngiap Chuan, director of research and Primary Care Research Institute at SHP.
A check by The Straits Times found that digital blood pressure monitors can cost anywhere between $50 and $300.
'This system also facilitates continuous remote health monitoring, allowing healthcare professionals to detect health issues before they become serious, enable earlier intervention, and potentially reducing hospitalisations,' said Prof Tan.
More than 100,000 people in South Korea have tested out DeepHealthVision, and around 30 healthcare professionals in Singapore were able to try their hand at it during a recent live demonstration .
The heart rate and blood pressure readings were around 96 per cent accurate during the local test, as compared to readings taken by Omron blood pressure monitors or Samsung Galaxy smartwatches, said injewelme's chief business development officer Nathan Cheong .
By analysing a 30-second video of a user's face, the tool is able to detect levels of blood flow in specific facial regions such as the forehead, cheeks, and nose.
'For example, someone who has high blood pressure or heart rate commonly presents with flushed skin,' said Mr Cheong, adding that the accuracy of measurement is not affected by skin colour. The team is also working to enable blood glucose monitoring and skin analysis in the future.
Around 60 polyclinic patients will be involved in a pilot test of this tool in the coming months, which would include those with and without hypertension, and others that are interested in knowing more about their own vascular health, said Prof Tan.
He said: 'We want to find out whether local patients are confident in using the technology, whether they accept it, and if they have any concerns. Because if they have concerns, they might not use it on a regular basis.'
The pilot test is expected to run for a minimum of one year , during which the data collected will be analysed and subjected to peer review.
The goal of the pilot is to have at least 96 per cent accuracy of readings, which would give clinicians and patients the confidence to use the tool, said Prof Tan. If the trial is successful, DeepHealthVision may be integrated into healthcare apps such as Health Buddy in the future.
Apart from injewelme, SHP also signed memorandums of understanding (MOUs) with two other South Korean companies, YoungAnd and Monit.
SHP is collaborating with YoungAnd on Brain Health Playground, an interactive platform designed for patients to play games that support cognitive function. Its collaboration with Monit focuses on a physical tool that uses sensors and an AI -driven pattern learning algorithm to assess diaper contamination levels .
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