The additional digital biomarkers will enhance the Empatica Health Monitoring Platform with new measures in categories including activity monitoring, energy expenditure, and sleep movements, and introduce key Functional Mobility Assessments such as the sit-to-stand test, gait test, timed up & go test, sway test, and stair test, which play a crucial role in assessing physical function and mobility in clinical care and research settings.

“This partnership shows a maturing digital health industry,” said Matteo Lai, Empatica’s CEO and Co-Founder. “Established players are joining forces to provide a one-stop solution to sponsors across the world. You can get the best medical technology and validated measures with a long clinical history in the same place: this means less setup time, costs and patient burden.”

“We are delighted to share the news of our partnership with Empatica,” said Martijn Niessen, CEO at McRoberts. “Combining our mobility analytics with their versatile platform and extensive array of physiological and behavioral biomarkers promises immense benefits for both our customers.”

The partnership cements the Empatica Health Monitoring Platform’s ability to be fully compatible with validated third-party algorithms. It is also the first known instance where an already established provider moves from using their proprietary wearable to a different device platform, allowing them to focus more energies on developing new endpoints. This showcases the confidence placed in Empatica’s technology, particularly in the monitoring capabilities of the EmbracePlus wearable. It also paves the path to additional partnerships, making it a true platform for other digital biomarker developers to have algorithms implemented in large-scale initiatives without needing to worry about the development of dedicated hardware and compatible software.

The McRoberts algorithms are available to all Enterprise plan users of the Empatica Health Monitoring Platform. Visit Empatica’s website to find out more, or reach out directly to Empatica at research@empatica.com.

About Empatica

Empatica Inc is a pioneer in continuous, unobtrusive remote health monitoring driven by AI. Empatica’s FDA-cleared platform and technology are used by thousands of institutional partners for research purposes, in studies examining stress, sleep, epilepsy, migraine, depression, addiction, and other conditions. Its flagship medical wearable, EmbracePlus, has been developed with key partners including HHS, USAMRDC, and the NASA-funded TRISH.

About McRoberts

McRoberts specializes in providing comprehensive solutions for assessing human mobility both in daily life and laboratory settings. McRoberts introduced its first algorithm and wearable sensor system for activity monitoring in 1994. Since then, their MoveMonitor and MoveTest solutions have been extensively utilized in clinical trials, healthcare, and academic research.

Contacts

Marianna Xenophontos
Empatica
mx@empatica.com

Study Details

The study analyzed blood samples from 52,645 healthy participants in the UK Biobank, collected between 2006 and 2010. After a decade, the frozen samples were thawed and studied for the presence of 1,463 proteins using artificial intelligence. By comparing patients who developed dementia with those who did not, the researchers identified a panel of 11 proteins that, when combined with other risk factors such as age, sex, education level, and genetics, proved to be over 90% effective in predicting dementia. The proteins were found to be indicative of various types of dementia, including Alzheimer’s disease and vascular dementia.

Potential Implications

The discovery of these biomarkers not only has the potential to reimagine early detection and diagnosis of dementia but also offers new avenues for research into novel treatments. Lead author Prof. Jianfeng Feng of the University of Warwick’s computer science department suggests that testing for these biomarkers could be integrated into the NHS as a screening tool by general practitioners (GPs). This could help identify individuals who would benefit from early treatment with disease-modifying therapies like Leqembi (lecanemab), which targets amyloid plaque and is currently under investigation.

Significance of the Study

The study found that glial fibrillary acidic protein (GFAP) could be used as a biomarker to predict dementia, even ten years prior to diagnosis. Another protein, LTBP2, showed a strong association with dementia, while NEFL also exhibited potential as a predictor. Although there are varying opinions on the strength of these associations, the study provides valuable insights into the biological systems involved in the development of dementia.

Implications for Future Research

While these findings are promising, further studies in diverse populations will be essential for validating the tests and predictive models. Alzheimer’s Research UK and the Alzheimer’s Society have recently launched the five-year Blood Biomarker Challenge project in collaboration with the National Institute of Health and Care Research (NIHCR) to gather the necessary information for introducing a blood test for dementia into the UK healthcare system. This initiative aims to enhance screening capabilities and ensure early intervention for at risk individuals.

The identification of protein biomarkers 15 years before the diagnosis of dementia represents a significant breakthrough in the field of dementia research. This study offers hope for early intervention and personalized treatment, allowing individuals to manage their condition effectively. While more research is needed to validate the findings and implement these biomarkers into clinical practice, these advancements bring us closer to transforming the diagnosis and management of dementia on a global scale.

Artificial intelligence (AI) is transforming healthcare in a myriad of ways, from diagnosing diseases to predicting outcomes and personalizing treatment plans. One of the most promising areas of AI in healthcare is the use of computer vision to analyze medical images and detect biomarkers of disease.

Researchers from the University of Southern California developed a deep learning system (DLS) that predicts systemic parameters related to the liver, kidney, bone or mineral, thyroid, and blood using external eye photographs as input.

The study analysed over 123,000 images of 38,398 diabetes patients across 11 sites in Los Angeles, California, and achieved statistically significant superior performance compared to baseline models in detecting multiple biomarkers.

This research has found that external eye photographs could be used to detect biomarkers for systemic medical conditions such as liver, kidney, bone or mineral, thyroid, and blood count.

By using deep learning systems, the team trained a model to identify systemic parameters including, estimated glomerular filtration rate, urine albumin-to-creatinine ratio, and white blood cells, among others.

The DLS was trained using 123,130 images from 38,398 diabetes patients undergoing diabetic eye screening. Results showed that the DLS outperformed baseline models for detecting various medical conditions, such as abnormal liver and kidney function and anemia.

The system was trained on data from three sources: clinics in the Los Angeles County Department of Health Services, Veterans Affairs primary care clinics in the greater Atlanta area, and community-based outpatient clinics in the Atlanta VA Healthcare System.

The researchers trained a convolutional neural network to take an external eye photograph as input and predict all clinical and laboratory measurements in a multitask fashion. The DLS identified nine parameters with clinical utility that it could predict with accuracy. These parameters included albumin, AST, calcium, eGFR, haemoglobin, platelets, TSH, urine ACR, and WBC.

The AI system was able to outperform a baseline model that only considered clinicodemographic variables for predicting several health conditions, including severely increased albuminuria and moderate anemia.

The researchers also conducted explainability experiments to determine which parts of the eye image were most important for the AI’s performance, showing that color information was at least somewhat important for most prediction targets.
This new technology has the potential to improve early detection and treatment of various health conditions, especially in areas with limited access to healthcare professionals.

The results showed that the DLS performed significantly better than baseline clinicodemographic models at predicting kidney function and blood count abnormalities across all three validation sets and at predicting abnormalities in liver and multiorgan parameters in validation set A.

The researchers believe that the tool could be best used in screening settings. In a young, healthy population, this may be used to detect moderate kidney disease or severe kidney dysfunction in an older population.

The DLS also outperformed the baseline for low hemoglobin detection. However, the absolute performance for liver (AST) and thyroid (TSH) abnormalities was lacklustre, with AUCs in the low 60s. The study’s limitations include that the datasets were primarily from diabetic retinopathy screening populations. In addition, all images were collected on fundus cameras.

This non-invasive screening method could enable early detection of systemic disease, but further work is needed to understand the translational implications.