Living With Frontotemporal Degeneration: Diagnostic Journey, Symptom Experiences, and Disease Impact

Barker, Megan S; Dodge, Shana; Niehoff, Debra; Denny, Sharon; Dacks, Penny A.; Dickinson, Susan; Cosentino, Stephanie; Wheaton, Dianna K.

doi:10.1177/08919887221119976

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…Frontotemporal Dementia (FTD) is a type of dementia distinguished by the deterioration of nerve cells in the frontal and temporal lobes of the brain, resulting in the shrinkage of these regions [6]. It impacts behaviour, attitude, language, and movement and is more prevalent in individuals under 65 years old, often affecting those between 40 and 65, but it can also occur in younger adults and older individuals [7].…”

Section: Fig 1 Types Of Dementiamentioning

confidence: 99%

Enhanced CBAMWDNet: A deep learning approach for accurate dementia multiclassification using MRI scans

Mohana,

Zuhaibuddin,

Hussain

et al. 2024

MATEC Web Conf.

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The rise in dementia cases emphasizes the critical need for accurate and early diagnosis. While numerous studies have focused on precise classification systems for singular dementia types, a gap exists in comprehensive classification encompassing various dementia subtypes. This research addresses this gap by curating a diverse MRI dataset containing multiple forms of dementia, aiming to develop a robust classification model. The research focuses on enhancing the CBAMWDNet, an advanced deep learning model, to precisely categorize different types of dementia like Alzheimer's, Lewy body, Frontotemporal and Vascular dementia. Originally developed for detecting tuberculosis in chest X-ray images, this model incorporates the architecture of Convolutional Block Attention Module (CBAM), Wide ResNet, and Dense blocks (WDnet). By leveraging a well-balanced and varied MRI dataset, the model's training will encompass a spectrum of dementia presentations, enhancing its capacity for nuanced classification. The proposed research aims not only to advance the capabilities of CBAMWDNet but also to contribute significantly to personalized medical diagnostics. Achieving accurate classification across diverse dementia subtypes holds the potential to revolutionize patient care, enabling tailored interventions and treatments based on precise subtype identification. This research thus underscores its relevance in the broader context of improving healthcare outcomes for individuals affected by dementia.

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Section: Fig 1 Types Of Dementiamentioning

confidence: 99%

Enhanced CBAMWDNet: A deep learning approach for accurate dementia multiclassification using MRI scans

Mohana,

Zuhaibuddin,

Hussain

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

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A Systematic Review on the Evidence of Misdiagnosis in Dementia and Its Impact on Accessing Dementia Care

Giebel,

Silva‐Ribeiro,

Watson

et al. 2024

Int J Geriat Psychiatry

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BackgroundWhilst there is a drive to increase diagnosis rates in dementia, there is a lack of attention on getting a correct and timely subtype diagnosis. For people with a rarer subtype of dementia, getting the correct diagnosis, and subsequent care, might be more difficult than for people aged 65+ presenting with the more common symptoms of Alzheimer's disease dementia. Thus, the aim of this mixed‐method systematic review was to synthesise the evidence base on misdiagnosis of dementia.MethodsMisdiagnosis in dementia was defined as either receiving an initial incorrect dementia subtype diagnosis or receiving an incorrect non‐dementia diagnosis. Post‐mortem assessments of subtype diagnosis were excluded. Nine databases were searched in June 2023, with screening of titles and abstracts and consequent full texts completed independently by two researchers. Findings were synthesised using narrative synthesis.ResultsTwenty studies were included. Studies were categorised into four themes: (i) Factors associated with delayed diagnosis or misdiagnosis; (ii) Difficulties related to the diagnostic process; (iii) Economic consequences of misdiagnosis; and (iv) Experiences of delayed diagnosis or help‐seeking. People with Lewy Body dementia or behavioural variant fronto‐temporal dementia were found to experience longer diagnosis times and often incorrect initial diagnoses. Whilst evidence is limited regarding the economic impacts, evidence from the US points towards increased economic costs of misdiagnosis.ConclusionsThere is an urgent need to investigate the rates and emotional and economic impacts of misdiagnosis on people with dementia, their carers, and the health and social care system. Advancing the evidence base is crucial to reduce misdiagnosis and inform clinical practice.

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A new model to understand the complexity of inequalities in dementia

Giebel

2024

Int J Equity Health

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Many people living with dementia and unpaid carers experience inequalities in care related to challenges in receiving a correct diagnosis, care and support. Whilst complexities of the evidence are well recognised including barriers in receiving a diagnosis or post-diagnostic care, no coherent model has captured the far-reaching types and levels of inequalities to date. Building on the established Dahlgren & Whitehead Rainbow model of health determinants, this paper introduces the new Dementia Inequalities model. The Dementia Inequalities model, similar to the original general rainbow model, categorises determinants of health and well-being in dementia into three layers: (1) Individual; (2) Social and community networks; and (3) Society and infrastructure. Each layer comprises of general determinants, which have been identified in the original model but also may be different in dementia, such as age (specifically referring to young- versus late-onset dementia) and ethnicity, as well as new dementia-specific determinants, such as rare dementia subtype, having an unpaid carer, and knowledge about dementia in the health and social care workforce. Each layer and its individual determinants are discussed referring to existing research and evidence syntheses in the field, arguing for the need of this new model. A total of 48 people with lived, caring, and professional experiences of dementia have been consulted in the process of the development of this model. The Dementia Inequalities model provides a coherent, evidence-based overview of inequalities in dementia diagnosis and care and can be used in health and social care, as well as in commissioning of care services, to support people living with dementia and their unpaid carers better and try and create more equity in diagnosis and care.

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Living With Frontotemporal Degeneration: Diagnostic Journey, Symptom Experiences, and Disease Impact

Cited by 4 publications

References 39 publications

Enhanced CBAMWDNet: A deep learning approach for accurate dementia multiclassification using MRI scans

Enhanced CBAMWDNet: A deep learning approach for accurate dementia multiclassification using MRI scans

A Systematic Review on the Evidence of Misdiagnosis in Dementia and Its Impact on Accessing Dementia Care

A new model to understand the complexity of inequalities in dementia

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