AcroViz's Dementia Prediction Model Demonstrates High Accuracy in Recent Study

Predictive cognitive decline for efficient prognosis and resource allocation to improve patients' wellness

TAIPEI, April 28, 2022 /PRNewswire/ -- An age-based dementia grading and predicting model by Taiwanese company AcroViz Inc. has demonstrated in a study its potential as an imaging biomarker used for grading dementia severity and predicting the change in this severity over the ensuing years.

AcroViz published a new study in the Journal of Alzheimer's Disease (JAD), showing brain-predicted age difference (PAD) is able to predict clinical dementia rating (CDR) change. Patients whose CDR changed from 0.5 to 1 in approximately two years had a mean PAD of 9.21 years.

The study was conducted by extracting structural brain data using diffusion magnetic resonance imaging (dMRI) and this data was fed into artificial intelligence algorithms for model training. The successfully trained and validated model was used to approximate the brain ages of study participants. This was then contrasted with the individuals' chronological ages to calculate the PAD. The PAD has demonstrated potential as a cognitive aging marker due to its association with cognitive decline and various cognitive functions.

Despite the heavy use of routine MRI technology in its PAD model, AcroViz's approach to grading dementia severity and predicting clinical worsening is distinguished from other players' by the way it uses MRI to diagnose its patients. While most use MRI brain imaging technology to measure atrophy and assess grey matter in a metroscopic view, AcroViz's technology uses its diffusion MRI analysis to reconstruct and assess axonal integrity in a sensitive microscopic view. The study found the accuracy of this prediction method to closely mirror patients' symptomatic statuses.

AcroViz's diffusion MRI analysis offers possibilities to be applied to other brain issues such as traumatic brain injury and chronic traumatic encephalopathy caused by war or sporting injuries where cognitive decline is typical. With AcroViz's age-based dementia model, clinicians can better address critical clinical needs and allocate medical resources to arrest cognitive decline. The data obtained from this objective assessment can differentiate patients' progression rates and enable the early application of various treatment plans.

"Accurately predicting cognitive decline will have a positively impactful prognosis. Not to mention, medical resources can be effectively utilised, while improving success rates in managing dementia related issues. This technology is a pivotal point in neurology; its possibilities outweigh current methods in the industry", said Chaur-Jong Hu, MD, Vice Superintendent of Taipei Neuroscience Institute, Taipei Medical University, and President of Taiwan Neurological Society.

The company believes in mitigating structural and psychological issues of the brain, supporting the development of interventions that can arrest or slow cognitive decline whilst placing emphasis on wellness that starts with a healthy brain. Presently, AcroViz Inc. has over a dozen partners in the health screening industry.

About AcroViz Inc.

AcroViz develops novel imaging biomarkers for brain health to prolong human healthspan with state-of-art technologies. Comprising eminent academic researchers and experienced industry professionals, the team is devoted to advancing scientific research, maximizing its clinical significance and application, as well as commercialization to achieve extended positive impact. The first product: AcroViz Axonal Brain Age – Brain Health Report, is the first to the market that adopts diffusion Magnetic Resonance Imaging (dMRI) and Machine Learning techniques to provide accurate and objective brain health assessment, which can serve as a Mild Cognitive Impairment (MCI) marker in predicting the risk and prognosis of dementia, such as Alzheimer's Disease (AD).

The published trial:

https://pubmed.ncbi.nlm.nih.gov/35094993/

Media Contact:

https:/acroviz/com/en/contact/

SOURCE Acroviz