AI Breakthrough: Outperforming Clinical Tests in Alzheimer's Prediction

Artificial intelligence outperforms clinical tests at predicting progress of Alzheimer's disease

Cambridge scientists have developed an artificially-intelligent tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer's disease. The team say this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best. Dementia poses a significant global health care challenge, affecting over 55 million people worldwide at an estimated annual cost of $820 billion. The number of cases is expected to almost treble over the next 50 years. The main cause of dementia is Alzheimer's disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests such as positron emission tomography (PET) scans or lumbar puncture, which are not available in all memory clinics. As a result, up to a third of patients may be misdiagnosed and others diagnosed too late for treatment to be effective. A team led by scientists from the Department of Psychology at the University of Cambridge has developed a machine learning model able to predict whether and how fast an individual with mild memory and thinking problems will progress to developing Alzheimer's disease. In research published in eClinicalMedicine, they show that it is more accurate than current clinical diagnostic tools. To build their model, the researchers used routinely-collected, non-invasive, and low-cost patient data -- cognitive tests and structural MRI scans showing gray matter atrophy -- from over 400 individuals who were part of a research cohort in the U.S.. They then tested the model using real-world patient data from a further 600 participants from the US cohort and -- importantly -- longitudinal data from 900 people from memory clinics in the UK and Singapore. The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer's disease within a three-year period. It was able to correctly identify individuals who went on to develop Alzheimer's in 82% of cases and correctly identify those who didn't in 81% of cases from cognitive tests and an MRI scan alone. The algorithm was around three times more accurate at predicting the progression to Alzheimer's than the current standard of care; that is, standard clinical markers (such as gray matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce misdiagnosis. The model also allowed the researchers to stratify people with Alzheimer's disease using data from each person's first visit at the memory clinic into three groups: those whose symptoms would remain stable (around 50% of participants), those who would progress to Alzheimer's slowly (around 35%) and those who would progress more rapidly (the remaining 15%). These predictions were validated when looking at follow-up data over six years. This is important as it could help identify those people at an early enough stage that they may benefit from new treatments, while also identifying those people who need close monitoring as their condition is likely to deteriorate rapidly. Importantly, those 50% of people who have symptoms such as memory loss but remain stable, would be better directed to a different clinical pathway as their symptoms may be due to other causes rather than dementia, such as anxiety or depression. Senior author Professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge said, "We've created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer's -- and if so, whether this progress will be fast or slow. "This has the potential to significantly improve patient well-being, showing us which people need closest care, while removing the anxiety for those patients we predict will remain stable. At a time of intense pressure on health care resources, this will also help remove the need for unnecessary invasive and costly diagnostic tests." While the researchers tested the algorithm on data from a research cohort, it was validated using independent data that included almost 900 individuals who attended memory clinics in the UK and Singapore. In the UK, patients were recruited through the Quantitative MRI in NHS Memory Clinics Study (QMIN-MC) led by study co-author Dr. Timothy Rittman at Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trusts (CPFT). The researchers say this shows it should be applicable in a real-world patient, clinical setting. Dr. Ben Underwood, Honorary Consultant Psychiatrist at CPFT and assistant professor at the Department of Psychiatry, University of Cambridge, said, "Memory problems are common as we get older. In clinic I see how uncertainty about whether these might be the first signs of dementia can cause a lot of worry for people and their families, as well as being frustrating for doctors who would much prefer to give definitive answers. "The fact that we might be able to reduce this uncertainty with information we already have is exciting and is likely to become even more important as new treatments emerge." Professor Kourtzi said, "AI models are only as good as the data they are trained on. To make sure ours has the potential to be adopted in a health care setting, we trained and tested it on routinely-collected data not just from research cohorts, but from patients in actual memory clinics. This shows it will be generalizable to a real-world setting." The team now hope to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and using different types of data, such as markers from blood tests. Professor Kourtzi added, "If we're going to tackle the growing health challenge presented by dementia, we will need better tools for identifying and intervening at the earliest possible stage. "Our vision is to scale up our AI tool to help clinicians assign the right person at the right time to the right diagnostic and treatment pathway. Our tool can help match the right patients to clinical trials, accelerating new drug discovery for disease modifying treatments."

Artificial intelligence outperforms clinical trials in predicting Alzheimer's disease progression

Scientists at the University of Cambridge have developed an artificial intelligence tool that can predict whether people with early signs of dementia will remain stable or develop Alzheimer's in four out of five cases. The researchers say this new approach could reduce the need for invasive and costly diagnostic tests and improve outcomes at an earlier stage, when interventions such as lifestyle changes and new drugs may be most effective. Dementia is a major global healthcare challenge, affecting more than 55 million people worldwide and costing an estimated $820 billion annually, with the number of patients expected to nearly triple over the next 50 years. The leading cause of dementia is Alzheimer's disease, accounting for 60-80% of cases. Early detection is important because treatment is likely to be most effective, but early diagnosis and prognosis of dementia may not be accurate without invasive or expensive tests such as a positron emission tomography (PET) scan or lumbar puncture, which are not available at all memory clinics. As a result, up to a third of patients may be misdiagnosed, and others may receive a diagnosis too late to benefit from treatment. A team led by scientists from the University of Cambridge's School of Psychology has developed a machine learning model that can predict whether people with mild memory or thought disorders will develop Alzheimer's disease, and how quickly they will develop it. Clinical Medicineand has been shown to be more accurate than current clinical diagnostic tools. To build the model, the researchers used routinely collected, non-invasive, low-cost patient data.Cognitive testing Structural MRI scans showing grey matter atrophy from over 400 subjects in a US study cohort. Then they Patient Data Additionally, data were obtained from 600 participants from a US cohort and, importantly, longitudinal data from 900 people from memory clinics in the UK and Singapore. The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer's disease within three years. Using only cognitive tests and MRI scans, it correctly identified those who went on to develop Alzheimer's disease in 82% of cases and those who did not in 81% of cases. The algorithm was approximately three times more accurate at predicting progression to Alzheimer's disease than the current standard of care, i.e. standard clinical markers (such as gray matter atrophy and cognitive scores) and clinical diagnosis, indicating that the model can significantly reduce misdiagnosis. This model allowed the researchers to classify Alzheimer's patients into three groups, using data from each person's first visit to the memory clinic: those whose symptoms remained stable (about 50% of participants), those whose Alzheimer's disease progressed more slowly (about 35%), and those whose Alzheimer's disease progressed more rapidly (the remaining 15%). These predictions were validated by looking at six years of follow-up data, which is important because it helps identify people early on who may benefit from new treatments, as well as those who need closer monitoring because their condition may worsen quickly. Importantly, the 50% of people who have symptoms such as memory loss but remain stable are better off being directed down a different clinical pathway as their symptoms may be caused by something other than dementia, such as anxiety or depression. Lead author Professor Zoe Curzi, from the University of Cambridge's School of Psychology, said: "Despite using only data from cognitive tests and MRI scans, we have developed a tool that is far more sensitive than current approaches in predicting whether mild symptoms will progress to Alzheimer's disease, and if so, whether that progression will be fast or slow." "This has the potential to significantly improve patient health outcomes by showing us which patients need the most attention and removing anxiety for those who are predicted to remain in a stable condition. At a time of increasing pressure on healthcare resources, this will also help to eliminate the need for unnecessary invasive and costly diagnostic tests." The researchers tested the algorithm on data from the study cohort, but the algorithm was then validated using independent data including about 900 people who attended memory clinics in the UK and Singapore. In the UK, patients were recruited through the NHS Memory Clinic Quantitative MRI Study (QMIN-MC), led by study co-author Dr Timothy Littman from Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trust (CPFT). The researchers say this shows potential application to real patients and clinical settings. Dr Ben Underwood, CPFT, Honorary Consultant Psychiatrist and Associate Professor of Psychiatry at the University of Cambridge, said: "Memory loss is a common symptom as people get older. In clinical practice we see that the uncertainty of whether this is the first sign of dementia can cause great anxiety for patients and their families, as well as frustrating situations for doctors who want to give definitive answers." "The fact that information we already have can potentially reduce this uncertainty is encouraging and is likely to become even more important as new treatments emerge." "An AI model is only as good as the data it is trained on," said Professor Kurzi. "To validate our model's potential for clinical adoption, we trained and tested it not only on a research cohort, but also on routinely collected data from real memory clinic patients. This shows that our AI model can be generalized to the real world." The team now hopes to extend the model to other types of dementia, such as vascular and frontotemporal dementia, using different types of data, such as blood test markers. "If we are to address the health challenges we face, dementiaWe need better tools to identify and intervene at the earliest possible stage. "Our vision is to extend AI tools to help clinicians put the right person on the right diagnosis and treatment pathway at the right time. Our tools will help match the right patients to clinical trials and accelerate the discovery of new medicines for disease-modifying treatments."

AI Predicts Alzheimer's Progression - Neuroscience News

Summary: A new AI tool predicts Alzheimer's progression with 82% accuracy using cognitive tests and MRI scans, outperforming current methods. This tool could reduce the need for costly tests and improve early intervention. Alzheimer's disease is the main cause of dementia, affecting over 55 million people worldwide. Cambridge scientists have developed an artificially-intelligent tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer's disease. The team say this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best. Dementia poses a significant global health care challenge, affecting over 55 million people worldwide at an estimated annual cost of $820 billion. The number of cases is expected to almost treble over the next 50 years. The main cause of dementia is Alzheimer's disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests such as positron emission tomography (PET) scans or lumbar puncture, which are not available in all memory clinics. As a result, up to a third of patients may be misdiagnosed and others diagnosed too late for treatment to be effective. A team led by scientists from the Department of Psychology at the University of Cambridge has developed a machine learning model able to predict whether and how fast an individual with mild memory and thinking problems will progress to developing Alzheimer's disease. In research published in eClinicalMedicine, they show that it is more accurate than current clinical diagnostic tools. To build their model, the researchers used routinely-collected, non-invasive, and low-cost patient data -- cognitive tests and structural MRI scans showing gray matter atrophy -- from over 400 individuals who were part of a research cohort in the U.S.. They then tested the model using real-world patient data from a further 600 participants from the US cohort and -- importantly -- longitudinal data from 900 people from memory clinics in the UK and Singapore. The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer's disease within a three-year period. It was able to correctly identify individuals who went on to develop Alzheimer's in 82% of cases and correctly identify those who didn't in 81% of cases from cognitive tests and an MRI scan alone. The algorithm was around three times more accurate at predicting the progression to Alzheimer's than the current standard of care; that is, standard clinical markers (such as gray matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce misdiagnosis. The model also allowed the researchers to stratify people with Alzheimer's disease using data from each person's first visit at the memory clinic into three groups: those whose symptoms would remain stable (around 50% of participants), those who would progress to Alzheimer's slowly (around 35%) and those who would progress more rapidly (the remaining 15%). These predictions were validated when looking at follow-up data over six years. This is important as it could help identify those people at an early enough stage that they may benefit from new treatments, while also identifying those people who need close monitoring as their condition is likely to deteriorate rapidly. Importantly, those 50% of people who have symptoms such as memory loss but remain stable, would be better directed to a different clinical pathway as their symptoms may be due to other causes rather than dementia, such as anxiety or depression. Senior author Professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge said, "We've created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer's -- and if so, whether this progress will be fast or slow. "This has the potential to significantly improve patient well-being, showing us which people need closest care, while removing the anxiety for those patients we predict will remain stable. At a time of intense pressure on health care resources, this will also help remove the need for unnecessary invasive and costly diagnostic tests." While the researchers tested the algorithm on data from a research cohort, it was validated using independent data that included almost 900 individuals who attended memory clinics in the UK and Singapore. In the UK, patients were recruited through the Quantitative MRI in NHS Memory Clinics Study (QMIN-MC) led by study co-author Dr. Timothy Rittman at Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trusts (CPFT). The researchers say this shows it should be applicable in a real-world patient, clinical setting. Dr. Ben Underwood, Honorary Consultant Psychiatrist at CPFT and assistant professor at the Department of Psychiatry, University of Cambridge, said, "Memory problems are common as we get older. In clinic I see how uncertainty about whether these might be the first signs of dementia can cause a lot of worry for people and their families, as well as being frustrating for doctors who would much prefer to give definitive answers. "The fact that we might be able to reduce this uncertainty with information we already have is exciting and is likely to become even more important as new treatments emerge." Professor Kourtzi said, "AI models are only as good as the data they are trained on. To make sure ours has the potential to be adopted in a health care setting, we trained and tested it on routinely-collected data not just from research cohorts, but from patients in actual memory clinics. This shows it will be generalizable to a real-world setting." The team now hope to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and using different types of data, such as markers from blood tests. Professor Kourtzi added, "If we're going to tackle the growing health challenge presented by dementia, we will need better tools for identifying and intervening at the earliest possible stage. "Our vision is to scale up our AI tool to help clinicians assign the right person at the right time to the right diagnostic and treatment pathway. Our tool can help match the right patients to clinical trials, accelerating new drug discovery for disease modifying treatments." Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic. We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely-collected, non-invasive, and low-cost (cognitive tests, structural MRI) patient data. To enhance scalability and generalizability to the clinic, we: 1) train the PPM with clinically-relevant predictors (cognitive tests, grey matter atrophy) that are common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore). PPM robustly predicts (accuracy: 81.66%, AUC: 0.84, sensitivity: 82.38%, specificity: 80.94%) whether patients at early disease stages (MCI) will remain stable or progress to Alzheimer's Disease (AD). PPM generalizes from research to real-world patient data across memory clinics and its predictions are validated against longitudinal clinical outcomes. PPM allows us to derive an individualized AI-guided multimodal marker (i.e. predictive prognostic index) that predicts progression to AD more precisely than standard clinical markers (grey matter atrophy, cognitive scores; PPM-derived marker: hazard ratio = 3.42, p = 0.01) or clinical diagnosis (PPM-derived marker: hazard ratio = 2.84, p < 0.01), reducing misdiagnosis. Our results provide evidence for a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for adoption in clinical practice. Wellcome Trust, Royal Society, Alzheimer's Research UK, Alzheimer's Drug Discovery Foundation Diagnostics Accelerator, Alan Turing Institute.

Revolutionary AI model improves early dementia diagnosis

University of CambridgeJul 13 2024 Cambridge scientists have developed an artificially-intelligent tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer's disease. The team say this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best. Dementia poses a significant global healthcare challenge, affecting over 55 million people worldwide at an estimated annual cost of $820 billion. The number of cases is expected to almost treble over the next 50 years. The main cause of dementia is Alzheimer's disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests such as positron emission tomography (PET) scans or lumbar puncture, which are not available in all memory clinics. As a result, up to a third of patients may be misdiagnosed and others diagnosed too late for treatment to be effective. A team led by scientists from the Department of Psychology at the University of Cambridge has developed a machine learning model able to predict whether and how fast an individual with mild memory and thinking problems will progress to developing Alzheimer's disease. In research published today in eClinical Medicine, they show that it is more accurate than current clinical diagnostic tools. To build their model, the researchers used routinely-collected, non-invasive, and low-cost patient data - cognitive tests and structural MRI scans showing grey matter atrophy - from over 400 individuals who were part of a research cohort in the USA. They then tested the model using real-world patient data from a further 600 participants from the US cohort and - importantly - longitudinal data from 900 people from memory clinics in the UK and Singapore. The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer's disease within a three-year period. It was able to correctly identify individuals who went on to develop Alzheimer's in 82% of cases and correctly identify those who didn't in 81% of cases from cognitive tests and an MRI scan alone. The algorithm was around three times more accurate at predicting the progression to Alzheimer's than the current standard of care; that is, standard clinical markers (such as grey matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce misdiagnosis. The model also allowed the researchers to stratify people with Alzheimer's disease using data from each person's first visit at the memory clinic into three groups: those whose symptoms would remain stable (around 50% of participants), those who would progress to Alzheimer's slowly (around 35%) and those who would progress more rapidly (the remaining 15%). These predictions were validated when looking at follow-up data over 6 years. This is important as it could help identify those people at an early enough stage that they may benefit from new treatments, while also identifying those people who need close monitoring as their condition is likely to deteriorate rapidly. Importantly, those 50% of people who have symptoms such as memory loss but remain stable, would be better directed to a different clinical pathway as their symptoms may be due to other causes rather than dementia, such as anxiety or depression. Senior author Professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge said: "We've created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer's - and if so, whether this progress will be fast or slow. "This has the potential to significantly improve patient wellbeing, showing us which people need closest care, while removing the anxiety for those patients we predict will remain stable. At a time of intense pressure on healthcare resources, this will also help remove the need for unnecessary invasive and costly diagnostic tests." While the researchers tested the algorithm on data from a research cohort, it was validated using independent data that included almost 900 individuals who attended memory clinics in the UK and Singapore. In the UK, patients were recruited through the Quantiative MRI in NHS Memory Clinics Study (QMIN-MC) led by study co-author Dr Timothy Rittman at Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trusts (CPFT). The researchers say this shows it should be applicable in a real-world patient, clinical setting. Dr Ben Underwood, Honorary Consultant Psychiatrist at CPFT and assistant professor at the Department of Psychiatry, University of Cambridge, said: "Memory problems are common as we get older. In clinic I see how uncertainty about whether these might be the first signs of dementia can cause a lot of worry for people and their families, as well as being frustrating for doctors who would much prefer to give definitive answers. The fact that we might be able to reduce this uncertainty with information we already have is exciting and is likely to become even more important as new treatments emerge." Professor Kourtzi said: "AI models are only as good as the data they are trained on. To make sure ours has the potential to be adopted in a healthcare setting, we trained and tested it on routinely-collected data not just from research cohorts, but from patients in actual memory clinics. This shows it will be generalizable to a real-world setting." The team now hope to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and using different types of data, such as markers from blood tests. Professor Kourtzi added: "If we're going to tackle the growing health challenge presented by dementia, we will need better tools for identifying and intervening at the earliest possible stage. Our vision is to scale up our AI tool to help clinicians assign the right person at the right time to the right diagnostic and treatment pathway. Our tool can help match the right patients to clinical trials, accelerating new drug discovery for disease modifying treatments." The study was funded by Wellcome, the Royal Society, Alzheimer's Research UK, the Alzheimer's Drug Discovery Foundation Diagnostics Accelerator, the Alan Turing Institute, and the National Institute for Health and Care Research Cambridge Biomedical Research Centre. University of Cambridge Journal reference: Lee, L. Y., et al. (2024) Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings. eClinicalMedicine. https://doi.org/10.1016/j.eclinm.2024.102725.

New AI tool could be game-changer in battle against Alzheimer's

A new AI tool can predict whether people with mild memory and mental agility problems are likely to go on to develop Alzheimer's disease in future - without the need for invasive or costly diagnostic tests. The tool would allow those at risk to modify their lifestyles or start new drug treatments at an early stage when they are most effective. It would also prevent inappropriate treatment of people with cognitive problems likely to be caused by other conditions, such as anxiety and depression. Scientists at the University of Cambridge used the artificially intelligent algorithm to analyse cognitive tests and MRI brain scans from 1,500 patients in the UK, USA and Singapore. It was able to distinguish people with mild mental agility problems that would remain stable from those who would progress to Alzheimer's disease over the following three years. The tool's prediction was more than 80% accurate, three times better than existing clinical methods for identifying patients likely to develop the disease, according to the study published in the journal eClinicalMedicine. Professor Zoe Kourtzi, the study's senior author, said the AI tool could also predict whether a patient's symptoms would deteriorate slowly or more rapidly. "This has the potential to significantly improve patient wellbeing, showing us which people need closest care, while removing the anxiety for those patients we predict will remain stable," she said. Being able to accurately identify patients likely to develop Alzheimer's by using only routine clinical data and MRI brain scans would be game-changing for the NHS. Currently an accurate diagnosis requires either an expensive PET brain scanner or a sample of spinal fluid taken by specially trained staff. The NHS is short of both. The lack of resources could hamper access to new drugs that can slow the progression of symptoms - but only if patients are diagnosed in the early stages of the disease. Read more science news How motherhood changes bodies forever Nights owls have 'better brain function than morning people' Three-legged lion makes 'longest swim ever' Dr Ben Underwood, honorary consultant psychiatrist at Cambridgeshire and Peterborough NHS Foundation Trust, said he frequently sees people with memory problems. "In clinic I see how uncertainty about whether these might be the first signs of dementia can cause a lot of worry for people and their families, as well as being frustrating for doctors who would much prefer to give definitive answers," he said. "The fact that we might be able to reduce this uncertainty with information we already have is exciting and is likely to become even more important as new treatments emerge." Alison Gilderdale first started repeating herself and struggling with her memory a decade ago. But it took six years for the symptoms to become clear enough for doctors to diagnose Alzheimer's. An earlier diagnosis would have helped her recognise what was happening to her. "I thought I was ok and it was everyone else saying 'she's not right'," she said. "Now I get lots of help. Things like getting dressed were difficult."