̽��ֱ�� of Cambridge - health

Handheld device could transform heart disease screening

sc604 — Tue, 08 Apr 2025 08:10:47 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, developed a device that makes it easy for people with or without medical training to record heart sounds accurately. Unlike a stethoscope, the device works well even if it’s not placed precisely on the chest: its larger, flexible sensing area helps capture clearer heart sounds than traditional stethoscopes.

̽��ֱ��device can also be used over clothing, making it more comfortable for patients – especially women – during routine check-ups or community heart health screening programmes.

̽��ֱ��heart sound recordings can be saved on the device, which can then be used to detect signs of heart valve disease. ̽��ֱ��researchers are also developing a machine learning algorithm which can detect signs of valve disease automatically. ̽��ֱ��results are reported in the IEEE Journal of Biomedical and Health Informatics.

Heart valve disease (valvular heart disease or VHD) has been called the ‘next cardiac epidemic,’ with a prognosis worse than many forms of cancer. Up to 50% of patients with significant VHD remain undiagnosed, and many patients only see their doctor when the disease has advanced and they are experiencing significant complications.

In the UK, the NHS and NICE have identified early detection of heart valve disease as a key goal, both to improve quality of life for patients, and to decrease costs.

An examination with a stethoscope, or auscultation, is the way that most diagnoses of heart valve disease are made. However, just 38% of patients who present to their GP with symptoms of valve disease receive an examination with a stethoscope.

“ ̽��ֱ��symptoms of VHD can be easily confused with certain respiratory conditions, which is why so many patients don’t receive a stethoscope examination,” said Professor Anurag Agarwal from Cambridge’s Department of Engineering, who led the research. “However, the accuracy of stethoscope examination for diagnosing heart valve disease is fairly poor, and it requires a GP to conduct the examination.”

In addition, a stethoscope examination requires patients to partially undress, which is both time consuming in short GP appointments, and can be uncomfortable for patients, particularly for female patients in routine screening programmes.

̽��ֱ��‘gold standard’ for diagnosing heart valve disease is an echocardiogram, but this can only be done in a hospital and NHS waiting lists are extremely long – between six to nine months at many hospitals.

“To help get waiting lists down, and to make sure we’re diagnosing heart valve disease early enough that simple interventions can improve quality of life, we wanted to develop an alternative to a stethoscope that is easy to use as a screening tool,” said Agarwal.

Agarwal and his colleagues have developed a handheld device, about the diameter of a drinks coaster, that could be a solution. Their device can be used by any health professional to accurately record heart sounds, and can be used over clothes.

While a regular or electronic stethoscope has a single sensor, the Cambridge-developed device has six, meaning it is easier for the doctor or nurse – or even someone without any medical training – to get an accurate reading, simply because the surface area is so much bigger.

̽��ֱ��device contains materials that can transmit vibration so that it can be used over clothes, which is particularly important when conducting community screening programmes to protect patient privacy. Between each of the six sensors is a gel that absorbs vibration, so the sensors don’t interfere with each other.

̽��ֱ��researchers tested the device on healthy participants with different body shapes and sizes and recorded their heart sounds. Their next steps will be to test the device in a clinical setting on a variety of patients, against results from an echocardiogram.

In parallel with the development of the device, the researchers have developed a machine learning algorithm that can use the recorded heart sounds to detect signs of valve disease automatically. Early tests of the algorithm suggest that it outperforms GPs in detecting heart valve disease.

“If successful, this device could become an affordable and scalable solution for heart health screening, especially in areas with limited medical resources,” said Agarwal.

̽��ֱ��researchers say that the device could be a useful tool to triage patients who are waiting for an echocardiogram, so that those with signs of valve disease can be seen in a hospital sooner.

A patent has been filed on the device by Cambridge Enterprise, the ̽��ֱ��’s commercialisation arm. Anurag Agarwal is a Fellow of Emmanuel College, Cambridge.

Reference:
Andrew McDonald et al. ‘A flexible multi-sensor device enabling handheld sensing of heart sounds by untrained users.’ IEEE Journal of Biomedical and Health Informatics (2025). DOI: 10.1109/JBHI.2025.3551882

Researchers have developed a handheld device that could potentially replace stethoscopes as a tool for detecting certain types of heart disease.

This device could become an affordable and scalable solution for heart health screening, especially in areas with limited medical resources

Anurag Agarwal

Acoustics Lab

Person demonstrating use of a handheld device for heart disease screening

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Cambridge is shaping AI for everyone

lw355 — Mon, 07 Apr 2025 08:15:22 +0000

Explore how Cambridge is using AI for better healthcare, smarter public services and new ways of tackling climate change. Meet our community and discover how ai@cam is supporting the development of AI that works for science, citizens and society.

Scientists develop ‘smart pyjamas’ to monitor sleep disorders

sc604 — Tue, 18 Feb 2025 11:06:44 +0000

̽��ֱ��team, led by the ̽��ֱ�� of Cambridge, developed printed fabric sensors that can monitor breathing by detecting tiny movements in the skin, even when the pyjamas are worn loosely around the neck and chest.

̽��ֱ��sensors embedded in the smart pyjamas were trained using a ‘lightweight’ AI algorithm and can identify six different sleep states with 98.6% accuracy, while ignoring regular sleep movements such as tossing and turning. ̽��ֱ��energy-efficient sensors only require a handful of examples of sleep patterns to successfully identify the difference between regular and disordered sleep.

̽��ֱ��researchers say that their smart pyjamas could be useful for the millions of people in the UK who struggle with disordered sleep to monitor their sleep, and how it might be affected by lifestyle changes. ̽��ֱ��results are reported in the Proceedings of the National Academy of Sciences (PNAS).

Sleep is vital for human health, yet more than 60% of adults experience poor sleep quality, leading to the loss of between 44 and 54 annual working days, and an estimated one percent reduction in global GDP. Sleep behaviours such as mouth breathing, sleep apnoea and snoring are major contributors to poor sleep quality, and can lead to chronic conditions such as cardiovascular disease, diabetes and depression.

“Poor sleep has huge effects on our physical and mental health, which is why proper sleep monitoring is vital,” said Professor Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “However, the current gold standard for sleep monitoring, polysomnography or PSG, is expensive, complicated and isn’t suitable for long-term use at home.”

Home devices that are simpler than PSG, such as home sleep tests, typically focus on a single condition and are bulky or uncomfortable. Wearable devices such as smartwatches, while more comfortable to wear, can only infer sleep quality, and are not effective for accurately monitoring disordered sleep.

“We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality,” said Occhipinti.

To develop the smart pyjamas, Occhipinti and his colleagues built on their earlier work on a smart choker for people with speech impairments. ̽��ֱ��team re-designed the graphene-based sensors for breath analysis during sleep, and made several design improvements to increase sensitivity.

“Thanks to the design changes we made, the sensors are able to detect different sleep states, while ignoring regular tossing and turning,” said Occhinpinti. “ ̽��ֱ��improved sensitivity also means that the smart garment does not need to be worn tightly around the neck, which many people would find uncomfortable. As long as the sensors are in contact with the skin, they provide highly accurate readings.”

̽��ֱ��researchers designed a machine learning model, called SleepNet, that uses the signals captured by the sensors to identify sleep states including nasal breathing, mouth breathing, snoring, teeth grinding, central sleep apnoea (CSA), and obstructive sleep apnoea (OSA). SleepNet is a ‘lightweight’ AI network, that reduces computational complexity to the point where it can be run on portable devices, without the need to connect to computers or servers.

“We pruned the AI model to the point where we could get the lowest computational cost with the highest degree of accuracy,” said Occhinpinti. “This way we are able to embed the main data processors in the sensors directly.”

̽��ֱ��smart pyjamas were tested on healthy patients and those with sleep apnoea, and were able to detect a range of sleep states with an accuracy of 98.6%. By treating the smart pyjamas with a special starching step, they were able to improve the durability of the sensors so they can be run through a regular washing machine.

̽��ֱ��most recent version of the smart pyjamas are also capable of wireless data transfer, meaning the sleep data can be securely transferred to a smartphone or computer.

“Sleep is so important to health, and reliable sleep monitoring can be key in preventative care,” said Occhipinti. “Since this garment can be used at home, rather than in a hospital or clinic, it can alert users to changes in their sleep that they can then discuss with their doctor. Sleep behaviours such as nasal versus mouth breathing are not typically picked up in an NHS sleep analysis, but it can be an indicator of disordered sleep.”

̽��ֱ��researchers are hoping to adapt the sensors for a range of health conditions or home uses, such as baby monitoring, and have been in discussions with different patient groups. They are also working to improve the durability of the sensors for long-term use.

̽��ֱ��research was supported in part by the EU Graphene Flagship, Haleon, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang, Wentian Yi et al. ‘A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.’ PNAS (2025). DOI: 10.1073/pnas.2420498122

Researchers have developed comfortable, washable ‘smart pyjamas’ that can monitor sleep disorders such as sleep apnoea at home, without the need for sticky patches, cumbersome equipment or a visit to a specialist sleep clinic.

We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality

Luigi Occhipinti

Illustration and photograph of 'smart pyjamas'

Yes

Supertroopers: CAR-T cell cancer therapy

lw355 — Wed, 16 Oct 2024 08:00:15 +0000

A life-saving cancer therapy is being scaled up in Cambridge to deliver more treatments to more patients for more cancers.

Team’s hip replacement surgery invention is set to be world first

sc604 — Fri, 13 Sep 2024 11:18:10 +0000

They have just won an award to develop their technology, which aims to make hip surgery more precise and deliver better and longer-lasting outcomes – which is good for patients and the NHS.

̽��ֱ��National Institute for Health and Care Research (NIHR) has awarded a £1.4 million Invention for Innovation (i4i) Product Development Award to advance work on the team’s “smart” joint “trial liner”.

̽��ֱ��sensors measure forces passing through the hip joint to help the surgeon assess and balance the soft tissues, which aids the accurate positioning of the implant.

Once measurements are complete using the wireless surgical aid, the surgeon marks the ideal position for the implant, removes the smart trial liner, and completes the operation.

There are currently no technologies that can deliver such readings during an operation and in real-time, and instead surgeons balance the joint based on feel and anatomical landmarks.

This is despite over two million total hip replacements being performed annually, with the number constantly rising due to increasing lifespans. Younger patients are starting to need hip replacements as well, so implants need to withstand higher stresses and last longer, to avoid spiralling into a vicious circle of revision surgery and higher rates of dissatisfaction.

Driving this clinical initiative is the chief investigator from Cambridge ̽��ֱ�� Hospitals (CUH) NHS Foundation Trust, Consultant orthopaedic surgeon, clinical and research lead of the Young Adult Hip Service, and Affiliate Associate Professor at the ̽��ֱ�� of Cambridge Vikas Khanduja.

̽��ֱ��technology development is being overseen by Professor Sohini Kar-Narayan from Cambridge’s Department of Materials Science and Metallurgy, together with Dr Jehangir Cama, who is leading on translational and commercialisation activities. They are joined by Consultant clinical scientist and CUH head of clinical engineering, Professor Paul White.

“We’re really looking forward to this next phase of product development that will see us move towards an actual product that is fit for clinical use, and that has the potential to revolutionise joint replacement surgery,” said Kar-Narayan.

“This funding will bring together wide-ranging expertise to help us further develop our prototype, bringing this technology closer to clinical use,” said Cama.

̽��ֱ��team currently has a prototype version of the device, which has been validated in the laboratory and in other tests. However, the NIHR award is important for further development and finalisation of the design and compliance with regulations before it can be tested in a living patient.

̽��ֱ��team’s underlying sensor technology intellectual property has been protected via a patent application filed by Cambridge Enterprise, the ̽��ֱ��’s commercialisation arm.

“This is a fantastic example of Cambridge’s entrepreneurial clinicians, academics and their institutions working together with forward-looking funders to create a positive impact for markets, society and importantly patients,” said Dr Terry Parlett, Commercialisation Director at Cambridge Enterprise.

Adapted from a CUH press release.

Technology that could transform the future of hip replacement surgery is being pioneered by a team of experts in Cambridge.

SEBASTIAN KAULITZKI/SCIENCE PHOTO LIBRARY via Getty Images

Illustration of a human hip joint

Yes

Five hubs launched to ensure UK benefits from quantum future

sc604 — Fri, 26 Jul 2024 06:30:07 +0000

̽��ֱ��hub, called Q-BIOMED, is one of 5 quantum research hubs announced on 26 July by Peter Kyle MP, the Secretary of State for Science, Innovation and Technology, supported by £160 million in funding.

̽��ֱ��hub will exploit advances in quantum sensors capable of detecting cells and molecules, potentially orders of magnitude more sensitively than traditional diagnostic tests.

This includes developing quantum-enhanced blood tests to diagnose infectious diseases and cancer quickly and cheaply using portable instruments, and sensors measuring tiny changes to the magnetic fields in the brain that have the potential to detect early markers of Alzheimer’s disease before symptoms occur.

Other research will include quantum-enhanced MRI scans, heart scanners and surgical and treatment interventions for early-stage and hard-to-treat cancers.

“Quantum technologies harness quantum physics to achieve a functionality or a performance which is otherwise unattainable, deriving from science which cannot be explained by classical physics,” said Hub Co-Director Professor Mete Atatüre, Head of Cambridge’s Cavendish Laboratory. “Q-BIOMED will be delivered by an outstanding team of researchers from academia, the NHS, charities, government and industry to exploit quantum-enhanced advances for human health and societal good.”

“Our hub aims to grow a new quantum for health innovation ecosystem in the UK, and has already shaped the UK's new Quantum Mission for Health,” said Hub Co-Director Professor Rachel McKendry, from the London Centre for Nanotechnology and Division of Medicine at UCL. “Our long-term vision is to accelerate the entire innovation pipeline from discovery research, to translation, adoption and implementation within the NHS and global health systems, for the benefit of patients and societal good.”

“Quantum sensing allows us to gather information at cellular and molecular levels with unprecedented sensitivity to electric and magnetic fields," said Dr Ljiljana Fruk from the Department of Chemical Engineering and Biotechnology, a member of the Q-BIOMED team. "I look forward to learning from colleagues and engaging in challenging discussions to develop more sensitive, affordable tools for doctors and patients, advancing the future of healthcare.”

Cambridge researchers are also involved in three of the other newly-announced hubs:

̽��ֱ��UK Hub for Quantum Enabled Position, Navigation and Timing (QEPNT), led by the ̽��ֱ�� of Glasgow, will develop quantum technologies which will be key for national security and critical infrastructure and sectors such as aerospace, connected and autonomous vehicles (CAVs), finance, maritime and agriculture. Luca Sapienza (Engineering), Louise Hirst (Materials Science and Metallurgy/Cavendish Laboratory) and Dave Ellis (Cavendish Laboratory) are part of the QEPNT team.
QCI3: Hub for Quantum Computing via Integrated and Interconnected Implementations, led by the ̽��ֱ�� of Oxford, aims to develop the technologies needed for the UK to play a key role in the development of quantum computers, a market estimated to be worth $1.3 trillion by 2030. Ulrich Schneider (Cavendish Laboratory), Helena Knowles (Cavendish Laboratory), and Chander Velu (Institute for Manufacturing) are part of the QCI3 team.
̽��ֱ��Integrated Quantum Networks (IQN) Quantum Technology Research Hub, led by Heriot-Watt ̽��ֱ��, will undertake research towards the ultimate goal of a ‘quantum internet’, globally interlinked quantum networks connecting multiple quantum computers to produce enormous computational power. Richard Penty, Adrian Wonfor and Qixiang Cheng (Engineering), Atatüre and Dorian Gangloff (Cavendish Laboratory) are part of the IQN team.

̽��ֱ��fifth hub, UK Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT), is led by the ̽��ֱ�� of Birmingham.

̽��ֱ��five hubs are delivered by the UKRI Engineering and Physical Sciences Research Council (EPSRC), with a £106 million investment from EPSRC, the UKRI Biotechnology and Biological Research Council, UKRI Medical Research Council, and the National Institute for Health and Care Research. Added to this are contributions from industry and other partners worth more than £54 million.

Peter Kyle, Secretary of State for Science, Innovation and Technology, said: “We want to see a future where cutting-edge science improves everyday lives. That is the vision behind our investment in these new quantum technology hubs, by supporting the deployment of technology that will mean faster diagnoses for diseases, critical infrastructure safe from hostile threats and cleaner energy for us all.

“This isn’t just about research; it’s about putting that research to work. These hubs will bridge the gap between brilliant ideas and practical solutions. They will not only transform sectors like healthcare and security, but also create a culture of accelerated innovation that helps to grow our economy.”

EPSRC Executive Chair Professor Charlotte Deane said: “Technologies harnessing quantum properties will provide unparalleled power and capacity for analysis at a molecular level, with truly revolutionary possibilities across everything from healthcare to infrastructure and computing.

“ ̽��ֱ��5 Quantum Technology Hubs announced today will harness the UK’s expertise to foster innovation, support growth and ensure that we capitalise on the profound opportunities of this transformative technology.”

A major new research hub led by the ̽��ֱ�� of Cambridge and UCL aims to harness quantum technology to improve early diagnosis and treatment of disease.

James Tye/UCL

L-R: Professor John Morton (UCL), Professor Rachel McKendry (UCL), Professor Mete Atatüre (Cambridge), Professor Eleni Nastouli (UCL)

Yes

Training AI models to answer ‘what if?’ questions could improve medical treatments

sc604 — Fri, 19 Apr 2024 08:02:54 +0000

Artificial intelligence techniques can be helpful for multiple medical applications, such as radiology or oncology, where the ability to recognise patterns in large volumes of data is vital. For these types of applications, the AI compares information against learned examples, draws conclusions, and makes extrapolations.

Now, an international team led by researchers from Ludwig-Maximilians-Universität München (LMU) and including researchers from the ̽��ֱ�� of Cambridge, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy.

̽��ֱ��researchers found that causal machine learning (ML) can estimate treatment outcomes – and do so better than the machine learning methods generally used to date. Causal machine learning makes it easier for clinicians to personalise treatment strategies, which individually improves the health of patients.

̽��ֱ��results, reported in the journal Nature Medicine, suggest how causal machine learning could improve the effectiveness and safety of a variety of medical treatments.

Classical machine learning recognises patterns and discovers correlations. However, the principle of cause and effect remains closed to machines as a rule; they cannot address the question of why. When making therapy decisions for a patient, the ‘why’ is vital to achieve the best outcomes.

“Developing machine learning tools to address why and what if questions is empowering for clinicians, because it can strengthen their decision-making processes,” said senior author Professor Mihaela van der Schaar, Director of the Cambridge Centre for AI in Medicine. “But this sort of machine learning is far more complex than assessing personalised risk.”

For example, when attempting to determine therapy decisions for someone at risk of developing diabetes, classical ML would aim to predict how probable it is for a given patient with a range of risk factors to develop the disease. With causal ML, it would be possible to answer how the risk changes if the patient receives an anti-diabetes drug; that is, gauge the effect of a cause. It would also be possible to estimate whether metformin, the commonly-prescribed medication, would be the best treatment, or whether another treatment plan would be better.

To be able to estimate the effect of a hypothetical treatment, the AI models must learn to answer ‘what if?’ questions. “We give the machine rules for recognising the causal structure and correctly formalising the problem,” said Professor Stefan Feuerriegel from LMU, who led the research. “Then the machine has to learn to recognise the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers.”

Even in situations for which reliable treatment standards do not yet exist or where randomised studies are not possible for ethical reasons because they always contain a placebo group, machines could still gauge potential treatment outcomes from the available patient data and form hypotheses for possible treatment plans, so the researchers hope.

With such real-world data, it should generally be possible to describe the patient cohorts with ever greater precision in the estimates, bringing individualised therapy decisions that much closer. Naturally, there would still be the challenge of ensuring the reliability and robustness of the methods.

“ ̽��ֱ��software we need for causal ML methods in medicine doesn’t exist out of the box,” says Feuerriegel. “Rather, complex modelling of the respective problem is required, involving close collaboration between AI experts and doctors.”

In other fields, such as marketing, explains Feuerriegel, the work with causal ML has already been in the testing phase for some years now. “Our goal is to bring the methods a step closer to practice,” he said. ̽��ֱ��paper describes the direction in which things could move over the coming years.”

“I have worked in this area for almost 10 years, working relentlessly in our lab with generations of students to crack this problem,” said van der Schaar, who is affiliated with the Departments of Applied Mathematics and Theoretical Physics, Engineering and Medicine. “It’s an extremely challenging area of machine learning, and seeing it come closer to clinical use, where it will empower clinicians and patients alike, is very satisfying.”

Van der Schaar is continuing to work closely with clinicians to validate these tools in diverse clinical settings, including transplantation, cancer and cardiovascular disease.

Reference:
Stefan Feuerriegel et al. ‘Causal machine learning for predicting treatments.’ Nature Medicine (2024). DOI: 10.1038/s41591-024-02902-1

Adapted from an LMU media release.

Machines can learn not only to make predictions, but to handle causal relationships. An international research team shows how this could make medical treatments safer, more efficient, and more personalised.

Yuichiro Chino via Getty Images

Computer-generated image of human brain

Yes

CamFest Speaker Spotlight: Dr Carmel McEniery

zs332 — Fri, 01 Mar 2024 10:15:55 +0000

Dr Carmel McEniery is a Senior Research Associate in the Division of Experimental Medicine and Immunotherapeutics at the ̽��ֱ�� of Cambridge and principal investigator on the Cambridge POPPY study into pre-eclampsia. She will be taking part in an event which will explore the puzzling relationship between pregnancy, pre-eclampsia and women’s long-term cardiovascular health.