Health start-up Medial EarlySign is utilising machine learning to support diabetic patients
Located in Kfar Malal, Israel, healthcare start-up Medial EarlySign has worked to develop a machine-learning solution to provide a long-term solution to diabetes patients. The technology will support the ongoing management of diabetes, improve clinical data and provide better patient outcomes.
Recent clinical data has highlighted and identified patients who are most at risk of having renal dsyfunction after just one year. The technology has analysed a number of areas within Electronic Health Records (EHRs), including laboratory tests results, demographics, medication, diagnostic codes and many more, to enable the prediction of those who are most at risk.
By isolating less than 5% of the 400,000-diabetic population selected among the company's database of 15 million patients, the algorithm was able to identify 45% of patients who would progress to significant kidney damage within a year, prior to becoming symptomatic. Kidney problems are one of the most common symptoms of diabetes-related complications.
- 3D printing is no longer the technology of the future – it’s now here to stay
- DiaMonTech and the development of non-invasive blood glucose monitoring
- Capgemini acquires digital customer engagement specialist LiquidHub
"Immense efforts are invested in developing treatment protocols to reduce the number of patients who will develop renal dysfunction due to diabetes," said Dr. Ran Goshen, Medial EarlySign's Chief Medical Officer.
"Medial EarlySign's algorithm can aid decision-makers, drug developers, insurers and providers to better allocate their capped resources and secure preferential clinical outcome as well. This can help reduce the likelihood for diabetes related end stage renal disease (ESRD)."
By leveraging EHR data, medical professionals can widen their clinical knowledge and expertise, redefine the management of chronic diseases, improve patient outcomes and lower healthcare costs.
Skin Analytics wins NHSX award for AI skin cancer tool
An artificial intelligence-driven tool that identifies skin cancers has received an award from NHSX, the NHS England and Department of Health and Social Care's initiative to bring technology into the UK's national health system.
NHSX has granted the Artificial Intelligence in Health and Care Award to DERM, an AI solution that can identify 11 types of skin lesion.
Developed by Skin Analytics, DERM analyses images of skin lesions using algorithms. Within primary care, Skin Analytics will be used as an additional tool to help doctors with their decision making.
In secondary care, it enables AI telehealth hubs to support dermatologists with triage, directing patients to the right next step. This will help speed up diagnosis, and patients with benign skin lesions can be identified earlier, redirecting them away from dermatology departments that are at full capacity due to the COVID-19 backlog.
Cancer Research has called the impact of the pandemic on cancer services "devastating", with a 42% drop in the number of people starting cancer treatment after screening.
DERM is already in use at University Hospitals Birmingham and Mid and South Essex Health & Care Partnership, where it has led to a significant reduction in unnecessary referrals to hospital.
Now NHSX have granted it the Phase 4 AI in Health and Care Award, making DERM available to clinicians across the country. Overall this award makes £140 million available over four years to accelerate the use of artificial intelligence technologies which meet the aims of the NHS Long Term Plan.
Dr Lucy Thomas, Consultant Dermatologist at Chelsea & Westminster Hospital, said: “Skin Analytics’ receipt of this award is great news for the NHS and dermatology departments. It will allow us to gather real-world data to demonstrate the benefits of AI on patient pathways and workforce challenges.
"Like many services, dermatology has severe backlogs due to the COVID-19 pandemic. This award couldn't have come at a better time to aid recovery and give us more time with the patients most in need of our help.”