An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study

Parminder Singh Reel; Salim Al-Wasity; Craig Edwards; Smarti Reel; Esma Mansouri-Benssassi; Szabolcs Suveges; Susan Krueger; Emily Jefferson; Alexander Doney; Douglas Steele

An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study

Parminder Singh Reel, Salim Al-Wasity, Craig Edwards, Smarti Reel, Esma Mansouri-Benssassi, Szabolcs Suveges, Susan Krueger, Emily Jefferson, Alexander Doney, Douglas Steele

Research output: Contribution to conference › Poster › peer-review

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Original language	English
Publication status	Published - 25 Feb 2025
Event	Alzheimer's Research UK Conference 2025 - ICC Birmingham, Birmingham, United Kingdom Duration: 25 Feb 2025 → 26 Feb 2025 https://www.alzheimersresearchuk.org/research/for-researchers/research-conference/scientific-programme-2025/

Conference

Conference	Alzheimer's Research UK Conference 2025
Abbreviated title	ARUK 2025
Country/Territory	United Kingdom
City	Birmingham
Period	25/02/25 → 26/02/25
Internet address	https://www.alzheimersresearchuk.org/research/for-researchers/research-conference/scientific-programme-2025/

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Final published versionFinal published version, 2.56 MB

MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)
Doney, A. (Investigator), Jefferson, E. (Investigator), Palmer, C. (Investigator), Steele, D. (Investigator), Trucco, M. (Investigator) & Wang, H. (Investigator)
Medical Research Council
1/08/19 → 28/02/25
Project: Research
The Scottish eHealth Informatics Research Centre (E-HIRCs) (Joint with Universities of Aberdeen, Glasgow, Edinburgh, Strathclyde, St Andrews & Leicester and ISD)
Colhoun, H. (Investigator), Donnan, P. (Investigator), Guthrie, B. (Investigator), Jefferson, E. (Investigator), MacDonald, T. (Investigator), McCowan, C. (Investigator), Morris, A. (Investigator), Pearson, E. (Investigator), Sullivan, F. (Investigator) & Swedlow, J. (Investigator)
Medical Research Council
1/03/13 → 31/12/18
Project: Research

Health Informatics Centre
Health and Clinical Services
Facility/equipment: Facility

Cite this

Reel, P. S., Al-Wasity, S., Edwards, C., Reel, S., Mansouri-Benssassi, E., Suveges, S., Krueger, S., Jefferson, E., Doney, A., & Steele, D. (2025). An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study. Poster session presented at Alzheimer's Research UK Conference 2025, Birmingham, United Kingdom.

Reel, Parminder Singh ; Al-Wasity, Salim ; Edwards, Craig et al. / An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach : A Feasibility Study. Poster session presented at Alzheimer's Research UK Conference 2025, Birmingham, United Kingdom.

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title = "An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study",

author = "Reel, {Parminder Singh} and Salim Al-Wasity and Craig Edwards and Smarti Reel and Esma Mansouri-Benssassi and Szabolcs Suveges and Susan Krueger and Emily Jefferson and Alexander Doney and Douglas Steele",

year = "2025",

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day = "25",

language = "English",

note = "Alzheimer's Research UK Conference 2025, ARUK 2025 ; Conference date: 25-02-2025 Through 26-02-2025",

url = "https://www.alzheimersresearchuk.org/research/for-researchers/research-conference/scientific-programme-2025/",

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Reel, PS, Al-Wasity, S, Edwards, C, Reel, S, Mansouri-Benssassi, E, Suveges, S , Krueger, S , Jefferson, E , Doney, A & Steele, D 2025, 'An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study', Alzheimer's Research UK Conference 2025, Birmingham, United Kingdom, 25/02/25 - 26/02/25.

An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study. / Reel, Parminder Singh; Al-Wasity, Salim; Edwards, Craig et al.
2025. Poster session presented at Alzheimer's Research UK Conference 2025, Birmingham, United Kingdom.

Research output: Contribution to conference › Poster › peer-review

TY - CONF

T1 - An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach

T2 - Alzheimer's Research UK Conference 2025

AU - Reel, Parminder Singh

AU - Al-Wasity, Salim

AU - Edwards, Craig

AU - Reel, Smarti

AU - Mansouri-Benssassi, Esma

AU - Suveges, Szabolcs

AU - Krueger, Susan

AU - Jefferson, Emily

AU - Doney, Alexander

AU - Steele, Douglas

PY - 2025/2/25

Y1 - 2025/2/25

M3 - Poster

Y2 - 25 February 2025 through 26 February 2025

ER -

Reel PS, Al-Wasity S, Edwards C, Reel S, Mansouri-Benssassi E, Suveges S et al.. An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study. 2025. Poster session presented at Alzheimer's Research UK Conference 2025, Birmingham, United Kingdom.

An end-to-end workflow for routine clinical MRI brain scans and linked prospective electronic medical records to predict future dementia using a Support Vector Machine (SVM) based Machine Learning Approach: A Feasibility Study

Conference

Access to Document

Projects

MICA: InterdisciPlInary Collaboration for EfficienT and Effective Use of Clinical Images in Big Data Health Care RESearch: PICTURES (Programme Grant) (Joint with Universities of Edinburgh and Abertay)

The Scottish eHealth Informatics Research Centre (E-HIRCs) (Joint with Universities of Aberdeen, Glasgow, Edinburgh, Strathclyde, St Andrews & Leicester and ISD)

Equipment

Health Informatics Centre

Cite this