Artificial intelligence in dementia

Abstract

Purpose of reviewArtificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. A comprehensive review of emerging trends will allow physicians and other healthcare providers to better anticipate and understand these powerful tools. Recent findingsMachine learning models that utilize cerebral biomarkers are demonstrably effective for dementia identification and prediction; however, cerebral biomarkers are relatively expensive and not widely available. As eye images harbor several ophthalmic biomarkers that mirror the state of the brain and can be clinically observed with routine imaging, eye-based machine learning models are an emerging area, with efficacy comparable with cerebral-based machine learning models. Emerging machine learning architectures like recurrent, convolutional, and partially pretrained neural networks have proven to be promising frontiers for feature extraction and classification with ocular biomarkers. SummaryMachine learning models that can accurately distinguish those with symptomatic Alzheimer's dementia from those with mild cognitive impairment and normal cognition as well as predict progressive disease using relatively inexpensive and accessible ocular imaging inputs are impactful tools for the diagnosis and risk stratification of Alzheimer's dementia continuum. If these machine learning models can be incorporated into clinical care, they may simplify diagnostic efforts. Recent advancements in ocular-based machine learning efforts are promising steps forward.

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

“…The development of AI, coupled with the utility of retinal imaging, could potentially be the answer to this difficult conundrum [ 69 ]. Using eye-based ML models for dementia identification has several advantages, including low cost, easy acquisition, and the non-invasive feature, which make retinal tests suitable for large-scale population screening and investigations of preclinical AD [ 70 ]. In one study, in an internal validation dataset, a DL model was found to have an accuracy of 83.6% (SD 2.5), a sensitivity of 93.2% (SD 2.2), a specificity of 82.0% (SD 3.1), and an AUROC of 0.93 (0.01) for the detection of AD.…”

Through the eyes into the brain, using artificial intelligence

“…33 For a long time, the retina has been considered a potential candidate for detecting neurodegenerative conditions, due to its high similarity and connections to the brain. 34,35 Similar to the brain, the retina is affected by ageing and neurodegenerative processes. However, a crucial question would be whether the pathological ageing of the retina reflects the nature and amount of associated brain lesions, especially in patients with dementia.…”

Introduction to Large Language Models (LLMs) for dementia care and research

“…To our knowledge, this is the first publication specifically reviewing LLMs in the context of dementia management and care. Previous reviews surveyed AI in dementia more broadly (de la Fuente Garcia et al, 2020 ; Lee et al, 2021 ; Richardson et al, 2022 ; Borchert et al, 2023 ; Tsoi et al, 2023 ) or focused on AI for prediction and early diagnosis (Stamate et al, 2020 ; Li et al, 2022 ; Merkin et al, 2022 ; Borchert et al, 2023 ).…”