Darmiyan is developing an AI-powered, high-throughput, cloud-based medical image processing platform for the early diagnosis and monitoring of Alzheimer’s disease.

CEO
Padideh Kamali-Zare
Founded
2016

Problem Tech Solves

Currently there is no single modality diagnostic standard of care for Alzheimer's disease/dementia. AD is diagnosed by a process of elimination, with clinicians relying on subjective cognitive tests, expensive and invasive PET scans, or genetic tests. These tests are often unreliable and in-conclusive at the early stages of the disease when current therapeutics and behavioral modifications such as an improved diet, better sleep or more exercise, maybe better able to delay the disease process. Specifically for patients - with an uncertain diagnosis of Alzheimer's disease say, BrainSee is able to provide a 5 year prognosis and accurate diagnosis. This is helpful for care planning and correct clinical management. BrainSee is also non-invasive and does not involve exposure to radiation, so a much easier procedure for patients to undergo.

Tech Brief

Darmiyan has developed BrainSee, a proprietary, AI-powered, robust, high-throughput, cloud-based medical image processing platform for early diagnosis and monitoring of Alzheimer’s disease in clinics and clinical trials. Additionally, BrainSee can be used to expedite drug development and facilitate clinical trials by accurate patient selection and drug effect monitoring. BrainSee works as a "Virtual Microscope", revealing key details of the human brain that are invisible to the human eye. Such features are reflective of the brain health status and indicative of neurodegenerative disease formation at very early stages. By combining MR image analysis, with cognitive test scores and using ML/AI techniques, we have developed the first platform which can become the standard of care for early diagnosis and monitoring of Alzheimer’s disease.

Tech Differentiators

Currently available diagnostic tools have major drawbacks, including: 1) Amyloid PET scans are invasive, involving radiotracer injection, radiation exposure, and have limited accessibility on top of being expensive. 2) MRI volumetry has low accuracy and is non-specific 3) Cognitive testing is subjective and not suitable for early diagnosis Also our BrainSee product (5-year prognosis of amnestic mild cognitive impairment and early detection of Alzheimer's disease pre-dementia): A) has got FDA Breakthrough Designation -- proving to the FDA committee the superiority of our solution to other solutions in market. B) is shown to be in the best interest of patients due to its non-invasiveness, wide accessibility around the world, and easy application in routine clinical workflow. C) is an AI that's 3rd party validated on real-world data from memory clinics and hospitals in the US and Canada showing high performance accuracy (91% = 20% higher than the state of the art) and high reliability (robustness)

Validation

BrainSee is third-party validated by highly reputable academic investigators from Stanford University, Baycrest Institute, Huntington Medical Research Institutes (HMRI), University Health Network (UHN), Knight Alzheimer's Disease Research Center (ADRC) at Washington University in St. Louis, and GERAS Hamilton Health Sciences (HHS). They confirmed that BrainSee performed with high accuracy, high test-retest reliability, and robustness to both research-grade and clinical-grade MRI scans. please find testimonials from the PIs of the project below: https://darmiyan.com/testimonials/ Also please find the 3rd party validation completion below: https://www.prnewswire.com/news-releases/the-first-virtual-microscope-of-the-human-brain-developed-by-darmiyan-accurately-measures-alzheimers-related-abnormalities-301109914.html

Why Us

Alzheimer's disease affects an aging population, which sadly lacks visibility. We are filling a huge unmet need for patients and caregivers of a disease with no proven DMT's, which could be the next pandemic. Our AI solution is unique; it combines 40+ years of neuroscience, medical imaging and machine learning.

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