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Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions

Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of t...

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Autores principales: Thakur, Siddhesh P., Schindler, Matthew K., Bilello, Michel, Bakas, Spyridon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968446/
https://www.ncbi.nlm.nih.gov/pubmed/35372431
http://dx.doi.org/10.3389/fmed.2022.797586
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author Thakur, Siddhesh P.
Schindler, Matthew K.
Bilello, Michel
Bakas, Spyridon
author_facet Thakur, Siddhesh P.
Schindler, Matthew K.
Bilello, Michel
Bakas, Spyridon
author_sort Thakur, Siddhesh P.
collection PubMed
description Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks: (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in: (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload.
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spelling pubmed-89684462022-04-01 Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions Thakur, Siddhesh P. Schindler, Matthew K. Bilello, Michel Bakas, Spyridon Front Med (Lausanne) Medicine Multiple Sclerosis (MS) is a demyelinating disease of the central nervous system that affects nearly 1 million adults in the United States. Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis and treatment monitoring in MS patients. In particular, follow-up MRI with T2-FLAIR images of the brain, depicting white matter lesions, is the mainstay for monitoring disease activity and making treatment decisions. In this article, we present a computational approach that has been deployed and integrated into a real-world routine clinical workflow, focusing on two tasks: (a) detecting new disease activity in MS patients, and (b) determining the necessity for injecting Gadolinium Based Contract Agents (GBCAs). This computer-aided detection (CAD) software has been utilized for the former task on more than 19, 000 patients over the course of 10 years, while its added function of identifying patients who need GBCA injection, has been operative for the past 3 years, with > 85% sensitivity. The benefits of this approach are summarized in: (1) offering a reproducible and accurate clinical assessment of MS lesion patients, (2) reducing the adverse effects of GBCAs (and the deposition of GBCAs to the patient's brain) by identifying the patients who may benefit from injection, and (3) reducing healthcare costs, patients' discomfort, and caregivers' workload. Frontiers Media S.A. 2022-03-17 /pmc/articles/PMC8968446/ /pubmed/35372431 http://dx.doi.org/10.3389/fmed.2022.797586 Text en Copyright © 2022 Thakur, Schindler, Bilello and Bakas. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Thakur, Siddhesh P.
Schindler, Matthew K.
Bilello, Michel
Bakas, Spyridon
Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
title Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
title_full Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
title_fullStr Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
title_full_unstemmed Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
title_short Clinically Deployed Computational Assessment of Multiple Sclerosis Lesions
title_sort clinically deployed computational assessment of multiple sclerosis lesions
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968446/
https://www.ncbi.nlm.nih.gov/pubmed/35372431
http://dx.doi.org/10.3389/fmed.2022.797586
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