Alzheimer’s Disease : Early detection by using AI

Alzheimer’s disease is a progressive neurological disorder that causes memory loss and other cognitive impairments. It affects millions of people around the world, and the number of cases is expected to rise in the coming years. Now, use of AI in early detection of Alzheimer’s disease and intervention are crucial in managing the symptoms and improving the quality of life for those affected by the disease.

Alzheimer’s Disease & AI

Undeniably, Artificial intelligence (AI) has emerged as a powerful tool in the early detection and diagnosis of Alzheimer’s disease. Using AI algorithms, researchers and healthcare professionals can analyze large amounts of data to identify patterns and markers that indicate the presence of the disease. This can help in identifying individuals who are at high risk of developing Alzheimer’s and initiate appropriate interventions at an early stage. 

Advantages of early detection

Obviously, One of the key advantages of using AI in the early detection of Alzheimer’s disease is its ability to analyze complex and multi-dimensional data sets. AI algorithms can process data from various sources, such as brain imaging scans, genetic tests, and cognitive assessments, to generate predictive models that can accurately predict the likelihood of developing Alzheimer’s. This can help in identifying individuals who may benefit from early interventions, such as lifestyle modifications, medication, and cognitive therapy. 

Additionally, AI can also help in monitoring the progression of Alzheimer’s disease over time. By analyzing longitudinal data from individuals with the disease, AI algorithms can track changes in cognitive function, behavior, and brain structure to provide insights into the progression of the disease. This can help in developing personalized treatment plans for individuals with Alzheimer’s and adjusting interventions based on their individual needs. 

Furthermore, AI can assist in the development of new diagnostic tools for Alzheimer’s disease. By training AI algorithms on large datasets of brain imaging scans, researchers can identify novel biomarkers and imaging signatures that are indicative of the disease. This can lead to the development of non-invasive and cost-effective diagnostic tests that can be used in clinical settings to detect Alzheimer’s at an early stage.

Use of AI beyond early detection

 In addition to early detection, AI can also play a crucial role in improving the care and management of individuals with Alzheimer’s disease. By analyzing data on medication adherence, treatment outcomes, and patient-reported outcomes, AI algorithms can identify patterns and trends that can help healthcare providers in optimizing treatment plans and improving patient outcomes. This can lead to better quality of care for individuals with Alzheimer’s and reduce the burden on caregivers and healthcare systems. Despite the potential benefits of using AI in the early detection and management of Alzheimer’s disease, there are challenges and limitations that need to be addressed.

Views of Researchers

UC San Francisco scientists have found a way to predict Alzheimer’s disease up to seven years before symptoms appear by analyzing patient records with machine learning.

The work demonstrates the promise of using artificial intelligence (AI) to spot patterns in clinical data that can then be used to scour large genetic databases to determine what is driving that risk. The researchers hope that one day it will hasten the diagnosis and treatment of Alzheimer’s and other complex diseases.

The conditions that most influenced the prediction were high cholesterol and, for women, the bone-weakening disease osteoporosis.

“This is a first step towards using AI on routine clinical data, not only to identify risk as early as possible, but also to understand the biology behind it,” said the study’s lead author, Alice Tang, an MD/PhD student in the Sirota Lab at UCSF. “The power of this AI approach comes from identifying risk based on combinations of diseases.”

The findings appear Feb. 21, 2024, in Nature Aging.

The use of artificial intelligence (AI) in early detection of Alzheimer’s disease is a promising area of research. Here are some notable developments:

New Developments(Alzheimer’s disease)

1. Speech Analysis Tool:
   • Researchers at UT Southwestern Medical Center developed a novel speech analysis tool that uses AI to detect mild cognitive impairment and dementia in a Spanish-speaking population¹.
   • By analyzing speech samples obtained during routine neuropsychological tests, this tool shows promise in quickly screening for signs of cognitive impairment.
   • Machine learning-based tools like this may play an increasingly important role in future cognitive screening for dementia.
2. Brain Scans and Structural Changes:
   • Another approach involves using brain scans to detect structural changes associated with Alzheimer’s disease.
   • Researchers at the University of Cambridge and The Alan Turing Institute developed machine learning tools that can identify dementia in patients at an early stage by analyzing brain scans².
   • These tools learn to spot subtle alterations in brain structures, potentially allowing for early intervention.
3. AI Algorithms and Patient Data:
   • AI algorithms can analyze extensive patient data, including medical records, imaging scans, and genetic information, to identify patterns and biomarkers indicative of Alzheimer’s disease³.
   • Clinicians can then make more informed diagnostic decisions and provide targeted treatments tailored to each patient’s unique condition.
4. Energy Usage Changes in Brain Scans:
   • Researchers at the University of California used over 2,000 brain scans to train an AI system to detect changes in energy usage.
   • The AI learned patterns from the scans, helping it identify signs of Alzheimer’s disease⁴.
   • In a test using 40 patients’ brain scans, the AI correctly assessed all cases.

In summary, AI holds great potential for early Alzheimer’s detection, whether through speech analysis, brain scans, or analyzing patient data. These advancements may lead to improved diagnosis and timely interventions .

AI in Drug Discovery

Researchers have made significant strides in using artificial intelligence (AI) for Alzheimer’s drug discovery. Here are some notable efforts:

1. Screening Existing Medications:
   • A team at Harvard-affiliated Massachusetts General Hospital (MGH) and Harvard Medical School developed an AI-based method called DRIAD (Drug Repurposing In Alzheimer’s Disease).
   • DRIAD screens currently available medications to identify potential treatments for Alzheimer’s disease.
   • By analyzing human brain neural cells’ responses to drugs, DRIAD correlates changes induced by drugs with disease severity markers.
   • This approach helps prioritize promising drugs for clinical trials and reveals new therapeutic targets.
2. Discovering New Targets:
   • Dr. Vendruscolo and Insilico Medicine’s AI platform, PandaOmics, enabled the FuzDrop method to discover three new targets associated with Alzheimer’s disease.
   • These targets pave the way for future drug development not only for Alzheimer’s but also for other diseases and cancers².
3. Hidden Data Mining:
   • Researchers at the University of California, San Francisco (UCSF) applied AI machine learning to archival disease databases.
   • By uncovering hidden data, they accelerate biotechnology research and drug discovery for Alzheimer’s and other conditions³.
4. Enzyme Design for Galantamine Synthesis:
   • UT at Austin researchers used AI machine learning to guide enzyme design for synthesizing galantamine, a drug used to treat dementia and Alzheimer’s.
   • This approach improves drug production and treatment options for patients⁴.

In summary, AI holds immense promise in identifying drug targets, repurposing existing medications, and accelerating Alzheimer’s drug discovery efforts.

Challenges

However, translating AI discoveries into effective treatments for Alzheimer’s disease faces several challenges:

1. Interpreting Electronic Health Record Data:
   • AI models often rely on electronic health records (EHRs) for training.
   • However, EHR data can be noisy, incomplete, and challenging to interpret accurately.
   • Ensuring reliable data quality remains a hurdle¹.
2. Cohort Selection Biases:
   • AI models learn from specific patient cohorts.
   • Biases in cohort selection can impact model generalization to broader populations.
   • Addressing these biases is crucial for robust AI applications¹.
3. Continuous Model Retraining:
   • AI models need regular updates to adapt to changing clinical practices.
   • Continuous retraining ensures models remain relevant and effective.
   • Balancing model stability and adaptability is essential¹.
4. Validation and Translation:
   • AI algorithms perform well in research settings but may struggle with real-world validation.
   • Translating research findings into clinical practice requires rigorous validation and integration.
   • Bridging this gap remains a challenge².
5. Complexity of Disease Mechanisms:
   • Alzheimer’s disease is multifactorial, involving genetic, environmental, and lifestyle factors.
   • AI must account for this complexity to identify effective treatments.
   • Understanding disease mechanisms and their interactions is an ongoing challenge³.
6. Heterogeneity of Dementia:
   • Dementia subtypes vary significantly, making personalized treatment challenging.
   • AI must address this heterogeneity to tailor interventions effectively.
   • Precision medicine approaches are essential².

Collaboration:

• Researchers, clinicians, and industry partners collaborate to bridge the gap between basic research and clinical practice.
• Initiatives like the Future Trends in Translational Medicine conference aim to motivate young researchers to transfer scientific results from laboratories to market and society.

• AI Integration in Clinical Trials:
  • Partnerships between technology and neuroscience specialists are transforming Alzheimer’s clinical trials.
  • Integrating AI helps address complex challenges, streamlining patient screening, predictive modeling, and trial efficiency.
• Patient Dropout Reduction:
  • High dropout rates in clinical trials pose challenges.
  • AI can analyze patient data to predict dropout risk, allowing for targeted interventions and improved trial retention.
• Precision Medicine and Personalized Treatment:
  • AI assists in identifying patient subgroups and tailoring treatments.
  • By considering individual variations, clinical trials can optimize outcomes for Alzheimer’s patients.

In summary, ongoing trials leverage AI, collaboration, and personalized approaches to overcome challenges and advance Alzheimer’s research and care

Breakthrough Therapies

Here are some noteworthy breakthrough therapies currently being investigated for Alzheimer’s disease:

1. Lecanemab (Leqembi™):
   • The FDA recently granted full approval to lecanemab, a new Alzheimer’s treatment.
   • In a Phase III clinical trial, lecanemab showed a 27% slowing of clinical decline after 18 months compared to a placebo.
   • It selectively targets toxic forms of amyloid protein in the brain, potentially slowing disease progression¹.
2. Plexin-B1 Protein Targeting:
   • Mount Sinai researchers discovered a potential method to treat Alzheimer’s by targeting the plexin-B1 protein.
   • This approach aims to improve plaque clearance in the brain, offering new therapeutic avenues².
3. Tau NexGen Combination Therapy:
   • Tau NexGen is an ongoing clinical trial testing a combination therapy for Alzheimer’s.
   • It addresses the heterogeneity of the disease and holds promise for halting disease progression³.

Scientists have made another major stride toward the long-sought goal of diagnosing Alzheimer’s disease with a simple blood test. On 6th July, 2024, a team of researchers reported that a blood test was significantly more accurate than doctors’ interpretation of cognitive tests and CT scans in signaling the condition.

The study, published in the journal JAMA, found that about 90 percent of the time the blood test correctly identified whether patients with memory problems had Alzheimer’s. Dementia specialists using standard methods that did not include expensive PET scans or invasive spinal taps were accurate 73 percent of the time, while primary care doctors using those methods got it right only 61 percent of the time.

“Not too long ago measuring pathology in the brain of a living human was considered just impossible,” said Dr. Jason Karlawish, a co-director of the Penn Memory Center at the University of Pennsylvania who was not involved in the research. “This study adds to the revolution that has occurred in our ability to measure what’s going on in the brain of living humans.”

The results, presented at the Alzheimer’s Association International Conference in Philadelphia, are the latest milestone in the search for affordable and accessible ways to diagnose Alzheimer’s, a disease that afflicts nearly seven million Americans and over 32 million people worldwide. Medical experts say the findings bring the field closer to a day when people might receive routine blood tests for cognitive impairment as part of primary care checkups, similar to the way they receive cholesterol tests.

In 2024, there are 171 ongoing studies and 134 drugs being tested in clinical trials, with over half aiming to be disease-modifying⁴. These efforts offer hope for improved treatments and better outcomes for patients. 

Wind Up

Conclusively, AI has the potential to revolutionize the early detection and management of Alzheimer’s disease. By leveraging AI algorithms to analyze complex and multi-dimensional data sets, researchers and healthcare providers can identify individuals at high risk of developing the disease, monitor disease progression, develop new diagnostic tools, and improve the care and management of individuals with Alzheimer’s. However, challenges such as standardized protocols, ethical considerations, and data security need to be addressed to ensure the safe and effective use of AI in healthcare. With continued research and collaboration, AI can help in advancing the field of Alzheimer’s disease and improving outcomes for individuals affected by the disease.

https://www.ucsf.edu/news/2024/02/427131/how-ai-can-help-spot-early-risk-factors-alzheimers-disease