Dementia is considered to be one of the top 6 leading causes of death in the United States currently. With an estimated 5 million Americans that are impacted by the diagnosis each year, the cost of treating dementia patients has reached us some of $259 billion annually.
Early diagnosis for dementia can reduce the economic impact of the medical condition. There are a number of therapies as well as arrangements that can be made that can make the process of living with the disease much easier. Researchers today are using Artificial Intelligence (AI) applications to provide an earlier diagnosis for dementia in the healthcare market.
What are some of the AI applications using to improve diagnosis accuracy?
AI systems are predicting the causes of dementia early by recognizing a few different symptom categories:
Medical imagery:
Machine learning out rhythms is now analyzing brain deterioration within scans to predict how quickly dementia can progress. This can help recognize when patients will need more hands-on care.
The Avalon AI, when paired with algorithms developed by Gonder’s Institute and Imperial College as well as the University of Cambridge, can detect small differences between medical images. This can lead to early recognition of abnormal anatomy changes and the chance to receive an earlier diagnosis and faster analysis. As this is a cloud-based program, testing can take as little as 30 min. For the upload and analysis ensuring that patients could get their answers much faster as well.
Visual indications:
Assessing the way a person moves their eyes in patterns can deliver insights into cognitive functions in brain activity as well as present symptoms for dementia diagnosis.
Neurotrack is a program that uses computer vision to monitor eye movement and continually track patterns which can indicate the way the brain is functioning. The company can complete a 5 minute test with their computer vision algorithm that tracks eye movement patterns and then delivers a direct correlation with cognitive functioning. What makes this cloud-based program even more exciting is that patients can perform the test with their WebCam at home receiving the results online.
Speech monitoring:
Detecting speech patterns with machine learning can help to monitor the progression of dementia and detect it early on.
Through companies like Winterlight and more, the analysis patterns can assess over 400 variables within speech and provide a record accuracy for discovering indicators of Alzheimer’s disease and dementia. With speech data alone there is an accuracy now available at roughly 82% from AI applications.
Genetics:
Using machine learning to analyze genetic data to predict the risk factors for dementia based off of someone’s genetic history is one aspect of treatment.
Applications such as aequa sciences as part of the University of Cambridge network are using machine learning algorithms to decipher genetic data from healthy patients and those that are affected by Alzheimer’s disease. This program is eventually recognizing ways to quantify the risk associated with the development of Alzheimer’s within our genetic makeup.
With a wide range of early AI systems that are turning their experience into the idea of Alzheimer’s development, we can start to see a dramatic shift in diagnosis and treatments for dementia in the future. AI could represent a new method for better diagnosis and reduced costs on the part of patients and medical facilities worldwide.
Reference: 1. https://www.techemergence.com/artificial-intelligence-dementia-diagnosis-genetic-analysis-speech-analysis/
2. https://www.imperial.ac.uk/news/186108/artificial-intelligence-improves-stroke-dementia-diagnosis/
3. https://medicalxpress.com/news/2018-04-artificial-intelligence-early-dementia.html
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
