New funding for wearable diagnostics for neuromuscular diseases
A researcher from the University of Glasgow has received funding to develop new wearable technology capable of measuring the progress of neuromuscular diseases. Professor Hadi Heidari has been awarded £1.8 million from the Engineering and the Engineering and Physical Sciences Research Council to develop a new wearable sensor system. It will provide less invasive, more accurate feedback on electrical activity inside the muscles of patients living with conditions like muscular dystrophy, Parkinson's disease and motor neurone disease. The project, called Super-Resolution non-invasive muscle measurements with miniaturised magnetic sensors, will develop a needle-free method of taking ultra-precise measurements of muscle activity. It will take the form of a wearable device similar to a smart watch that can measure patients' muscle activity using magnetomyography, or MMG.
MMG monitors the tiny magnetic fields created by muscles when contract or relax. Working with colleagues at the University of Edinburgh, Prof Heidari and his team at the University of Glasgow's Microelectronics Lab will develop a new MMG sensor based on cutting-edge sensing technology and a new microchip which will use artificial intelligence to pick MMG signals out of background noise. After the first 2 years of research to validate the technology, it will be field-tested over the following 3 years with patients in neuromuscular treatment clinics at the Queen Elizabeth University Hospital in Glasgow, Ostschweizer Children's Hospital in Switzerland and University Hospital Tübingen in Germany.
Majority of medical and nursing students planning careers outside patient care
A new report, Clinician of the Future 2023: Education Edition, provides insights into the experiences of medical and nursing students and the consequences for the NHS and educators in the UK.
The report reveals how the challenges of being a front-line health professional are affecting the longer-term career decisions of students, highlighting increasingly challenging times ahead for an already overburdened healthcare system.
Almost 9 out of 10 medical and nursing students in the UK feel devoted to improving patients' lives, findings reveal that 58% already see their current studies as a stepping stone towards a broader career in healthcare that will not involve directly caring for patients.
The report presents a worrying snapshot of the pressures that medical and nursing students feel today alongside the impending alarm they have for life in clinical practice:
- 20% of medical and nursing students in the UK are considering quitting their undergraduate studies
- 57% of medical and nursing students in the UK report they are worried about their mental wellbeing
- 64% of medical and nursing students in the UK believe they will suffer from clinician burnout
- 75% of medical and nursing students in the UK are concerned about healthcare staff shortages and the impact this would have on them in their future role.
Anjola Awe, a 3rd-year medical student at King's College London, said: ‘I have always known that I wanted to be a doctor; I've dedicated my life to getting to this stage. Honestly, the findings and themes in the report are not at all surprising to me or my peers; we know only too well the reality of what being a frontline healthcare professional means today.
‘We need to be vocal about the pressure we are under, the state of our mental health, and the volume of our academic workload.’
As of June this year, there were more than 125 000 vacancies in secondary care in England, and over 10% of all nursing posts remain unfilled. Despite the NHS treating more patients than before the pandemic, the waiting list for NHS treatment currently stands at a record 7.75 million. Against this backdrop, it is unsurprising that medical and nursing students in the UK are concerned about the challenges they will face in the future.
Dr Philip Xiu, a GP and educational lead who supports medical students and junior doctors in Leeds, West Yorkshire, said:
‘As an educator, I see daily how vital it is we address students’ well-being concerns. Doubling enrolment won't resolve looming workforce shortages if issues impeding student wellness go unresolved.
‘We must equip them with critical thinking to aid sound clinical decisions, and partnership skills to involve patients meaningfully in care. By listening and responding to students' needs, we can graduate resilient, empowered clinicians ready to elevate healthcare.’
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AI can predict if and when people at high risk progress to glaucoma
Artificial intelligence that is trained to recognise red flags in retinal images and clinical information can predict if and when people at high risk of glaucoma, usually referred to as ‘glaucoma suspects,’ go on to develop it, finds research published online in the British Journal of Ophthalmology.
Subject to further refinement with larger numbers of people, this may prove a helpful diagnostic aid for doctors, conclude the researchers.
Recent advances in AI have prompted the design of algorithms to better detect glaucoma progression. But none has so far drawn on clinical features to predict disease progression among people at high risk, point out the researchers. Glaucoma is one of the leading causes of blindness worldwide. It is difficult for doctors to know if and when people with suspicious signs of early optic nerve damage, but without the cardinal diagnostic feature of abnormally high internal pressure in the eye (intraocular pressure or IOP) will go on to develop glaucoma and risk losing their sight, they explain.
All three algorithms performed well and were able to consistently predict progression to glaucoma, and when, with a high degree of accuracy: 91–99%. The average age of participants at the start of the monitoring period was 55, ranging from 33 to 76. Baseline age did not emerge as a key predictive factor, but the average age of those who progressed to glaucoma was significantly lower than that of those who did not. The authors conclude, ‘Our results suggest that [deep learning] models that have been trained on both ocular images and clinical data have a potential to predict disease progression in [glaucoma suspect] patients.
‘We believe that with additional training and testing on a larger dataset, our [deep learning] models can be made even better, and that with such models, clinicians would be better equipped to predict individual [glaucoma suspect] patients’ respective disease courses.
‘Prediction of disease course on an individual-patient basis would help clinicians to present tailored management options to patients with regard to issues such as follow-up duration, starting (or not) of IOP-lowering treatment, and targeting of IOP levels.’