A new innovative medical device, known as EchoGo Pro, has been developed by the UK-based health tech firm, Ultromics, to use artificial intelligence technology to predict coronary artery disease in patients.
How does EchoGo Pro work?
According to the World Health Organisation, approximately 17 million deaths per year are caused by cardiovascular diseases, making it the number one cause of death around the world.
EchoGo Pro is able to detect heart disease by using AI-operated ultrasound analysis to scan images of the heart and identify the presence of a cardiovascular condition.
Diagnosing heart disease is an extremely complicated process, due to complex symptoms, comorbidities and circumstances that make it difficult for health professionals to identify the disease visually. In fact, approximately 1 in 5 patients are misdiagnosed with heart disease due to the difficulty in diagnosing it.
The new AI technology has the ability to analyse several thousand data points, as opposed to the 6 or so visual indicators that are currently used for diagnosis.
EchoGo Pro can then produce results in real-time, using its cloud computing system to send a cardiovascular disease diagnosis to hospitals via individual health services.
After US and UK trials of the new technology, EchoGo pro was found to reduce the number of incorrect diagnoses by half, in comparison to reports of current clinical practice.
The potential impact of EchoGo Pro on the UK’s healthcare system
The device can now be used across the UK and the EU as it is now CE marked to meet EU health, environmental protection and safety requirements.
According to Ross Upton, the CEO and co-founder of Ultromics, distribution of the new technology could not only save lives but also save resources and money for healthcare providers and hospitals around the world.
The other co-founder of Ultromics and head of the Oxford cardiovascular clinical research facility, Professor Paul Leeson, said: “Leveraging AI technology means we can more accurately predict heart disease and optimise care pathways – to help make valuable cost and time savings for healthcare systems at this time when they are already stretched and in much need of support.”
What’s this about machine learning and fertility treatment?
Another AI startup firm is taking machine learning to a new level by using it to improve women’s health care. Health science company Presagen has created scalable machine learning that can be used all over the world.
Machine learning is the application of artificial intelligence that gives systems the ability to automatically improve from experiences without being programmed to.
Recently, machine learning has been applied to women’s health tech, an area which research has traditionally fallen behind.
Presagen has created Life Whisperer, a cloud-based web application. It utilises machine learning to give patients who are undergoing fertility treatment better outcomes.
IVF was introduced in 1978, and since then over 8 million children have been conceived in this way. However, it is not always successful. For those over 35, the chance of giving birth to a baby with IVF is under 20% per cycle.
Life Whisperer aims to help doctors choose the most viable embryos for implantation, which can lessen the time that patients have to wait by 15%. Michelle Perungini, CEO of Presagen, states:
“The average age of pregnancy is increasing right at the point when fertility is naturally decreasing. Our vision is to improve the success rates for IVF and allow greater access at a lower cost for patients so that they can achieve that goal of having a family sooner.”
The system uses a camera connected to a microscope which sends pictures of the embryo via the cloud to Life Whisperer. These images are then viewed by computer vision algorithms, which can identify the parts of the embryo that can’t be seen by the naked eye but are key in determining viability.
Within 15 seconds the application gives a report which then assists the doctor in selecting the best embryo for implementation.
The future of fertility
The machine learning algorithms have improved the accuracy of embryo selection by 25%. These success rates are encouraging, and suggest that machine learning could improve fertility treatments. The technology is set to expand into the UK, Europe and India.
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