Krishnadas, P; Smith, N (2023) An Investigation of Available Medical Imaging Data for the Early Detection of Neurodegenerative Diseases. NPL Report. MS 50

Preview

Text (NPL Report MS 50) MS50.pdf - Published Version Download (290kB) | Preview

Abstract

Dementia is characterized by the acquired loss of cognitive and emotional abilities to an extent that it interrupts and inconveniences everyday life. It is not a disease, but rather, is used to describe a group of symptoms that occur when the brain cells stop working properly. Dementia has a prolonged onset period and can go unnoticed for years before significant symptoms manifest or a diagnosis. An early diagnosis for patients with dementia or its subtypes could open the door to better treatment and care as well as give patients the time and opportunity to plan their future while they still can do so. Neuroimaging techniques such as brain magnetic resonance imaging (MRI) and positron emission tomography (PET) are currently used as one of the ‘gold standard’ tools for diagnosing dementia causing diseases. However, these methods can be invasive, carry a risk to the patient, are time consuming and a burden on healthcare and financial resources. For this reason, the Early Detection of Neurodegenerative diseases (EDoN) initiative aims to develop an alternate approach for the large-scale early identification of individuals at risk for dementia causing diseases in a low burden, cost effective manner. EDoN is collecting digital data (e.g., from wearables and smartphone apps) and low burden clinical measures (e.g., blood tests) to use with machine learning models that can detect specific dementia causing diseases decades before noticeable cognitive symptoms manifest

In order to validate the new detection methods and digital biomarkers developed within EDoN, a comparison against the ‘gold standard’ biomarkers coming from neuroimages is needed. In this report we provide an overview of medical imaging data relevant to dementia causing diseases. We explore the availability of neuroimaging data and open access or access upon request databases. We elaborate on the challenges of accessing the data and give the details of those databases we were able to gain access to, in terms of number of subjects as well as their age and gender, and the imaging modalities used.

Item Type:	Report/Guide (NPL Report)
NPL Report No.:	MS 50
Subjects:	Mathematics and Scientific Computing > Modelling
Divisions:	Data Science
Identification number/DOI:	10.47120/npl.MS50
Last Modified:	16 May 2023 12:28
URI:	http://eprintspublications.npl.co.uk/id/eprint/9722

Actions (login required)

View Item