One of the biggest challenges the aging population faces is Alzheimer’s disease and its life-changing and devastating impacts. AI is proving to be a vital tool in diagnosing Alzheimer’s disease well before it makes its presence known. Here’s how.
SwissCognitive Guest Blogger: Zachary Amos – “How AI Helps Detect Alzheimer’s Disease”
Alzheimer’s disease has life-changing and devastating impacts. It is emotionally distressing for patients and their loved ones, so early detection is key to maximising their quality of life and independence. The sooner they can access symptom-managing treatments and supportive care plans, the better equipped they will be to deal with the effects of this life-changing diagnosis. Artificial intelligence (AI) is proving to be a vital tool in diagnosing Alzheimer’s disease well before it makes its presence known.
What Makes Early Detection Challenging?
Early detection of Alzheimer’s is challenging for several reasons. Its initial symptoms mimic normal aging, making them easy to overlook. Physicians use a combination of diagnostic tools, including cognitive and functional assessments, brain imaging, neurological exams, and cerebrospinal fluid or blood tests. It can be a lengthy and stressful process for patients. In addition, clinicians’ limited time and resources also contribute to diagnostic delays.
Researchers are making progress in using the full power of AI to develop more sensitive, scalable solutions for today’s world. In what ways is AI helping?
How AI Is Revolutionising Alzheimer’s Detection
Many researchers believe it can take up to 17 years to transform breakthrough bench research into protocols used in clinics. This is way too long for people desperate for help. AI assists by:
- Enhancing the detection of Alzheimer’s through neuroimaging and genetic and proteomic analysis.
- Accelerating treatment discovery and personalising it by predicting individual responses.
- Integrating multimodal data to forecast disease progression with deep learning.
Putting AI to work in these ways helps researchers make progress and gives people hope for their future.
Real-World Applications and Success Stories
Several universities and research centres are making strides in using AI to detect Alzheimer’s disease. Here are some of the top breakthroughs.
University of California, San Diego
With the help of AI, University of California, San Diego researchers have uncovered a potential therapeutic candidate for Alzheimer’s disease, although FDA studies are still necessary. They found that a gene initially recognised as a potential blood biomarker for Alzheimer’s called phosphoglycerate dehydrogenase (PHGDH) actually causes the disorder. It also activates a pathway that alters how cells in the brain activate genes, which can lead to disease development.
The researchers used AI to visualise the 3D structure of the PHGDH protein. They discovered that expression levels of this gene directly correlated with changes in the brain, and the higher the levels of protein and RNA produced by the PHGDH gene, the more advanced the disease. Therefore, tests are underway to inhibit this process and potentially treat or prevent Alzheimer’s.
Darmiyan
In 2024, the U.S. Food and Drug Administration approved the AI tool BrainSee, which uses brain scans to predict the likelihood of Alzheimer’s disease progression. This breakthrough provides a noninvasive screening solution that can be used before other tests or treatments. BrainSee is a scalable, fully automated software platform that combines MRIs and cognitive tests to generate a score that predicts the likelihood of progression from mild impairment to Alzheimer’s within five years.
University of California, San Francisco
In another development, the University of California, San Francisco, has developed an AI system that can detect Alzheimer’s disease with 89% accuracy. In 2018, researchers built predictive models by tapping into UCSF Medical Centre’s electronic health databases and used machine learning algorithms to forecast Alzheimer’s development six years ahead of time. This study highlights how artificial AI has the potential to significantly advance the understanding of and ability to diagnose complicated medical disorders.
West Virginia University
West Virginia University researchers have also made strides. They published a study on using 150 diagnostic metabolic biomarkers to develop AI tools that can detect Alzheimer’s disease early. These biomarkers can determine risk factors and treatment interventions, and the study sought to find which ones are most relevant to Alzheimer’s. This knowledge can be used to train an AI model that predicts the likelihood of the disorder developing.
AI-Related Challenges and Ethical Considerations
As promising as these breakthroughs are, using AI for Alzheimer’s detection faces significant ethical challenges. Bias in data collection could lead to health inequalities, and many express privacy concerns over the sensitive medical information gathered for studies.
In addition, offering predictive tests to asymptomatic individuals could cause problems and emotional distress. This is due to the current lack of treatment for people with potential cognitive decline, possible inaccuracies in AI-aided prediction, and the lack of transparency and explainability in AI decision-making. Addressing these issues requires a great deal of effort, including robust data governance, bias mitigation, clear consent protocols and ensuring AI is used responsibly to support the human element in patient care.
AI’s Promise in the Fight Against Alzheimer’s
AI shows great potential to aid in the early detection of Alzheimer’s disease and potentially find treatment options. More research is needed to make the most of this technology and to wield it responsibly and ethically. However, these advancements provide hope to people facing the possibility of developing this devastating and life-changing disorder.
About the Author:
Zac Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other tech topics.
One of the biggest challenges the aging population faces is Alzheimer’s disease and its life-changing and devastating impacts. AI is proving to be a vital tool in diagnosing Alzheimer’s disease well before it makes its presence known. Here’s how.
SwissCognitive Guest Blogger: Zachary Amos – “How AI Helps Detect Alzheimer’s Disease”
What Makes Early Detection Challenging?
Early detection of Alzheimer’s is challenging for several reasons. Its initial symptoms mimic normal aging, making them easy to overlook. Physicians use a combination of diagnostic tools, including cognitive and functional assessments, brain imaging, neurological exams, and cerebrospinal fluid or blood tests. It can be a lengthy and stressful process for patients. In addition, clinicians’ limited time and resources also contribute to diagnostic delays.
Researchers are making progress in using the full power of AI to develop more sensitive, scalable solutions for today’s world. In what ways is AI helping?
How AI Is Revolutionising Alzheimer’s Detection
Many researchers believe it can take up to 17 years to transform breakthrough bench research into protocols used in clinics. This is way too long for people desperate for help. AI assists by:
Putting AI to work in these ways helps researchers make progress and gives people hope for their future.
Real-World Applications and Success Stories
Several universities and research centres are making strides in using AI to detect Alzheimer’s disease. Here are some of the top breakthroughs.
University of California, San Diego
With the help of AI, University of California, San Diego researchers have uncovered a potential therapeutic candidate for Alzheimer’s disease, although FDA studies are still necessary. They found that a gene initially recognised as a potential blood biomarker for Alzheimer’s called phosphoglycerate dehydrogenase (PHGDH) actually causes the disorder. It also activates a pathway that alters how cells in the brain activate genes, which can lead to disease development.
The researchers used AI to visualise the 3D structure of the PHGDH protein. They discovered that expression levels of this gene directly correlated with changes in the brain, and the higher the levels of protein and RNA produced by the PHGDH gene, the more advanced the disease. Therefore, tests are underway to inhibit this process and potentially treat or prevent Alzheimer’s.
Darmiyan
In 2024, the U.S. Food and Drug Administration approved the AI tool BrainSee, which uses brain scans to predict the likelihood of Alzheimer’s disease progression. This breakthrough provides a noninvasive screening solution that can be used before other tests or treatments. BrainSee is a scalable, fully automated software platform that combines MRIs and cognitive tests to generate a score that predicts the likelihood of progression from mild impairment to Alzheimer’s within five years.
University of California, San Francisco
In another development, the University of California, San Francisco, has developed an AI system that can detect Alzheimer’s disease with 89% accuracy. In 2018, researchers built predictive models by tapping into UCSF Medical Centre’s electronic health databases and used machine learning algorithms to forecast Alzheimer’s development six years ahead of time. This study highlights how artificial AI has the potential to significantly advance the understanding of and ability to diagnose complicated medical disorders.
West Virginia University
West Virginia University researchers have also made strides. They published a study on using 150 diagnostic metabolic biomarkers to develop AI tools that can detect Alzheimer’s disease early. These biomarkers can determine risk factors and treatment interventions, and the study sought to find which ones are most relevant to Alzheimer’s. This knowledge can be used to train an AI model that predicts the likelihood of the disorder developing.
AI-Related Challenges and Ethical Considerations
As promising as these breakthroughs are, using AI for Alzheimer’s detection faces significant ethical challenges. Bias in data collection could lead to health inequalities, and many express privacy concerns over the sensitive medical information gathered for studies.
In addition, offering predictive tests to asymptomatic individuals could cause problems and emotional distress. This is due to the current lack of treatment for people with potential cognitive decline, possible inaccuracies in AI-aided prediction, and the lack of transparency and explainability in AI decision-making. Addressing these issues requires a great deal of effort, including robust data governance, bias mitigation, clear consent protocols and ensuring AI is used responsibly to support the human element in patient care.
AI’s Promise in the Fight Against Alzheimer’s
AI shows great potential to aid in the early detection of Alzheimer’s disease and potentially find treatment options. More research is needed to make the most of this technology and to wield it responsibly and ethically. However, these advancements provide hope to people facing the possibility of developing this devastating and life-changing disorder.
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