Diagnosing dementia, particularly for Alzheimer's Disease (AD) and frontotemporal dementia (FTD), is complex due to overlapping symptoms. While magnetic resonance imaging (MRI) and positron emission tomography (PET) data are critical for the diagnosis, integrating these modalities in deep learning faces challenges, often resulting in suboptimal performance compared to using single modalities. Moreover, the potential of multi-modal approaches in differential diagnosis, which holds significant clinical importance, remains largely unexplored. We propose a novel framework, DiaMond, to address these issues with vision Transformers to effectively integrate MRI and PET. DiaMond is equipped with self-attention and a novel bi-attention mechanism that synergistically combine MRI and PET, alongside a multi-modal normalization to reduce redundant dependency, thereby boosting the performance. DiaMond significantly outperforms existing multi-modal methods across various datasets, achieving a balanced accuracy of 92.4% in AD diagnosis, 65.2% for AD-MCI-CN classification, and 76.5% in differential diagnosis of AD and FTD. We also validated the robustness of DiaMond in a comprehensive ablation study. The code is available at https://github.com/ai-med/DiaMond.

The rapid global aging trend has led to an increase in dementia cases, including Alzheimer's disease, underscoring the urgent need for early and accurate diagnostic methods. Traditional diagnostic techniques, such as cognitive tests, neuroimaging, and biomarker analysis, face significant limitations in sensitivity, accessibility, and cost, particularly in the early stages. This study explores the potential of machine learning (ML) as a transformative approach to enhance early dementia detection by leveraging ML models to analyze and integrate complex multimodal datasets, including cognitive assessments, neuroimaging, and genetic information. A comprehensive review of existing literature was conducted to evaluate various ML models, including supervised learning, deep learning, and advanced techniques such as ensemble learning and transformer models, assessing their accuracy, interpretability, and potential for clinical integration. The findings indicate that while ML models show significant promise in improving diagnostic precision and enabling earlier interventions, challenges remain in their generalizability, interpretability, and ethical deployment. This research concludes by outlining future directions aimed at enhancing the clinical utility of ML models in dementia detection, emphasizing interdisciplinary collaboration and ethically sound frameworks to improve early detection and intervention strategies for Alzheimer's disease and other forms of dementia.

Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algorithms of Random Forest and Elastic Net are applied to train, optimise, and validate predictive models based on the English Longitudinal Study of Ageing - ELSA cohort. The two survival machine learning models are compared with the conventional statistical Cox proportional hazard model, proving their superior predictive capability and stability on the ELSA data, as demonstrated by computationally intensive procedures such as nested cross-validation and Monte Carlo validation. This study is the first to apply survival machine learning to the ELSA data, and demonstrates in this case the superiority of AI based predictive modelling approaches over the widely employed Cox statistical approach in survival analysis. Implications, methodological considerations, and future research directions are discussed.

Recent investigations show that large language models (LLMs), specifically GPT-4, not only have remarkable capabilities in common Natural Language Processing (NLP) tasks but also exhibit human-level performance on various professional and academic benchmarks. However, whether GPT-4 can be directly used in practical applications and replace traditional artificial intelligence (AI) tools in specialized domains requires further experimental validation. In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis. Comprehensive comparisons between GPT-4 and traditional AI tools are conducted to examine their diagnostic accuracy in a clinical setting. Experimental results on two real clinical datasets show that, although LLMs like GPT-4 demonstrate potential for future advancements in dementia diagnosis, they currently do not surpass the performance of traditional AI tools. The interpretability and faithfulness of GPT-4 are also evaluated by comparison with real doctors. We discuss the limitations of GPT-4 in its current state and propose future research directions to enhance GPT-4 in dementia diagnosis.

Scientists from the University of Surrey and the University of Newcastle have shown that it is possible to use electroencephalography (EEG) as a low-cost diagnostic tool to help clinicians identify different forms of dementia, including Lewy body, Alzheimer's and Parkinson's dementia. "Our study shows that using artificial intelligence analysis of EEG data as a diagnostic tool to identify dementia could be life-changing for many people. We have shown that by combining brain activity captured from patients with their eyes open and with their eyes closed, our machine learning algorithms can accurately detect different forms of dementia, including Lewy body dementia, which is often only found post-mortem. As a result, we believe that our method could allow people to be diagnosed and treated sooner. "The clear next step for our project is to gather support for a clinical trial for this incredibly promising technology."

Different forms of dementia could be spotted sooner and more easily by analysing recordings of patients' electrical brain activity using artificial intelligence (AI), according to new research. Scientists from the University of Surrey and the University of Newcastle have shown that it is possible to use electroencephalography (EEG) as a low-cost diagnostic tool to help clinicians identify different forms of dementia, including Lewy body, Alzheimer's and Parkinson's dementia. "Our study shows that using artificial intelligence analysis of EEG data as a diagnostic tool to identify dementia could be life-changing for many people. We have shown that by combining brain activity captured from patients with their eyes open and with their eyes closed, our machine learning algorithms can accurately detect different forms of dementia, including Lewy body dementia, which is often only found post-mortem. As a result, we believe that our method could allow people to be diagnosed and treated sooner.

To solve the conundrum of how to get timely medical care to people with memory loss or other impaired cognitive functioning, a new study suggests that artificial intelligence may be as accurate as clinicians in taking the first step: diagnosis. Findings from the study, which was conducted by researchers at Boston University School of Medicine, were published online Monday in the journal Nature Communications. "We're trying to leverage AI to create frameworks to mimic neurology experts," for dementia diagnosis, Vijaya B. Kolachalama, the study's principal investigator and assistant professor of medicine and computer science at Boston University, told UPI. He said his lab aims to use computer models to assist clinical practice. Kolachalama stressed that the aim of his team's work is to help reduce the workload of the busy neurology practice, not replace the expert clinician.

Using data from more than 15,300 patients in the US, research from the University of Exeter found that a form of artificial intelligence called machine learning can accurately tell who will go on to develop dementia. The technique works by spotting hidden patterns in the data and learning who is most at risk. The study, published in JAMA Network Open and funded by funded by Alzheimer's Research UK, also suggested that the algorithm could help reduce the number of people who may have been falsely diagnosed with dementia. The researchers analysed data from people who attended a network of 30 National Alzheimer's Coordinating Center memory clinics in the US. The attendees did not have dementia at the start of the study, though many were experiencing problems with memory or other brain functions.

Machine learning algorithms have been trained to predict the likelihood of a person developing dementia with 92 per cent accuracy, a study has concluded. Using data from over 15,300 patients in the US, research from Exeter University found that machine learning can accurately tell who will go on to develop dementia within two years of attending a memory clinic. The technique identifies hidden patterns in the data and learning who is most at risk. The study, published in JAMA Network Open and funded by funded by Alzheimer's Research UK, also suggested that the algorithm could help reduce the number of people who may have been falsely diagnosed with dementia. The researchers analysed data from people who attended a network of 30 National Alzheimer's Coordinating Center memory clinics in the US.

Artificial intelligence can reveal with incredible accuracy which individuals may develop dementia, new research has found. AI has a 92% accuracy rating for predicting which memory clinic attendees will have dementia within two years, according to the study, published Thursday in the journal JAMA Network Open. The findings are based on data from over 15,300 US patients. Authors say the algorithmic accuracy of AI predictions may be able to reduce the amount of false dementia diagnoses -- and possibly help doctors intervene earlier. "We know that dementia is a highly feared condition. Embedding machine learning in memory clinics could help ensure diagnosis is far more accurate, reducing the unnecessary distress that a wrong diagnosis could cause," said study co-author and University of Exeter research fellow Janice Ranson in a press release.