Study Published in Nature Digital Medicine Shows Potential of Machine Learning and Augmented Reality-based Digital Biomarkers in Alzheimer’s Detection

Digital Biomarkers for Alzheimer’s Disease: Early Diagnosis Means Early Treatment

August 17, 2021Neelem Sheikh

Biomarkers are the heart of disease diagnostics. A biomarker, or biological marker, refers to something that can be measured to reliably and accurately indicate the presence and severity of a disease or condition. Biomarkers include a wide array of measurable indicators ranging anywhere from an elevated white blood cell count to indicate infection to the presence of beta-amyloid plaques in the brain to indicate Alzheimer’s disease.

With the rise of digital health data collection, researchers and providers alike are embracing the potential of digital biomarkers. The newer concept of digital biomarkers, as the name suggests, refers to the idea of collecting clinically meaningful data through digital devices. Digital biomarkers may provide a new, more robust method for monitoring and diagnosing neurological diseases and other conditions, and they enable the collection and analysis of physiological and behavioral data which may be used for predictive diagnosis of diseases, such as Alzheimer’s. 

Digital biomarkers for Alzheimer’s disease may facilitate early diagnosis as well as the ability for Alzheimer’s patients to gain access to treatments and therapies earlier on when the disease is more treatable, providing better health outcomes. Let’s take a deeper look into digital biomarkers, their significance, and the future of Alzheimer’s disease diagnosis.

What Are Digital Biomarkers?

Digital biomarkers are quantifiable, objective physiological and behavioral data that are collected and measured via a digital device. Such devices include portables, wearables, implantables, or digestibles. Digital biomarkers yield new and robust data sets that can be used to learn more about the nuances of specific diseases and gain valuable health insight.  

Passive data from sensors integrated into wearable devices, such as smartwatches, is generated when a user simply wears the device. The data collected is then referred to as passive digital biomarker data. Similarly, digital biomarker data can be generated and captured from smart devices, such as smartphones and tablets, when a user interacts with the device in response to an active prompt. Integrated sensors including cameras, microphones, touchscreen sensors, accelerometers, and gyroscopes can be used to collect active digital biomarker data.

While wearables collect more obvious data—such as heart rate, heart rate variability, and oxygen saturation levels from photoplethysmography sensors—smartphones and tablets collect less intuitive, yet incredibly powerful data. Here are some examples of digital biomarker data that can be collected from smart devices:

  • Microphones can be used to detect biomarkers of speech, such as fluency, mood, and sentiment.
  • Cameras can detect eye movements, pupil dilation, and facial expression.
  • Touchscreen sensors can probe for fine motor skills required for tapping, swiping, and typing.
  • Inertial sensors, including accelerometers and gyroscopes, can detect human motion and posture, enabling the measurement of gait metrics.

The Significance of Digital Biomarkers

As more and more individuals embrace newer health-related technologies, the amount of available health data is growing at a staggering rate. When this volume of data is paired with strong analytical tools, it can potentially be leveraged to track trends and patterns for many diseases, including Alzheimer’s disease. Artificial intelligence can be used to detect such patterns and build models that weigh large data sets of digital biomarkers to identify the presence of neurological disease. Digital biomarkers for Alzheimer’s disease may enable early diagnosis and consequently, early intervention. At their core, digital biomarkers offer significant value toward better monitoring and diagnostic tools for Alzheimer’s disease. 

  • Longitudinal data: Digital biomarkers can provide longitudinal data collection on both an individual and population level. Most tools used to assess brain health lack the infrastructure for longitudinal analysis and typically only provide a means of cross-sectional data collection and analysis. Longitudinal data offers the ability to analyze brain health on a personalized basis to gain insights into how an individual’s brain health is changing over time. With longitudinal data—which tracks the progression of digital biomarkers over time—artificial intelligence can make predictions from data to determine if, when, and how an individual will develop Alzheimer’s disease.
  • Clinically significant data: Digital biomarkers offer the opportunity to collect objective and clinically significant data in a cost-effective and non-invasive way. Traditional biomarkers require expensive and invasive tests and procedures to collect, and analysis that depends on subjective and observational assessments. 
  • Preventive approach: Digital biomarkers can transform healthcare for neurological diseases like Alzheimer’s from a responsive approach to a preventive approach. Early analysis and monitoring of brain health can provide early detection of neurological disease, presenting the opportunity to delay the onset of the disease or intervene with lifestyle changes or therapies.

Can Digital Biomarkers for Alzheimer’s Disease Enable Early Diagnosis?

The potential to use digital biomarkers for Alzheimer’s disease to enable early diagnosis lies in the ability to determine which biomarkers are clinically significant as well as finding a powerful method to analyze large quantities of data.

Altoida’s mission is to accelerate and improve drug development, neurological disease research, and patient care. To learn more about our precision-neurology platform and app-based medical device, contact us!

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