Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps

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Standard

Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. / Jørgensen, Dan Richter; Dam, Erik Bjørnager; Lillholm, Martin.

I: Computers in Biology and Medicine, Bind 43, Nr. 8, 2013, s. 1045-1052.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Jørgensen, DR, Dam, EB & Lillholm, M 2013, 'Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps', Computers in Biology and Medicine, bind 43, nr. 8, s. 1045-1052. https://doi.org/10.1016/j.compbiomed.2013.05.012

APA

Jørgensen, D. R., Dam, E. B., & Lillholm, M. (2013). Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. Computers in Biology and Medicine, 43(8), 1045-1052. https://doi.org/10.1016/j.compbiomed.2013.05.012

Vancouver

Jørgensen DR, Dam EB, Lillholm M. Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. Computers in Biology and Medicine. 2013;43(8):1045-1052. https://doi.org/10.1016/j.compbiomed.2013.05.012

Author

Jørgensen, Dan Richter ; Dam, Erik Bjørnager ; Lillholm, Martin. / Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. I: Computers in Biology and Medicine. 2013 ; Bind 43, Nr. 8. s. 1045-1052.

Bibtex

@article{705067d66c794325b61de77b67a53776,
title = "Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps",
abstract = "This study investigates whether measures of knee cartilage thickness can predict future loss of knee cartilage. A slow and a rapid progressor group was determined using longitudinal data, and anatomically aligned cartilage thickness maps were extracted from MRI at baseline. A novel machine learning framework was then trained using these maps. Compared to measures of mean cartilage plate thickness, group separation was increased by focusing on local cartilage differences. This result is central for clinical trials where inclusion of rapid progressors may help reduce the period needed to study effects of new disease-modifying drugs for osteoarthritis. (C) 2013 Elsevier Ltd. All rights reserved.",
keywords = "Osteoarthritis, Knee MRI, Cartilage thickness, Clinical trials, Machine learning, Classification, Spatial data mining, Dimensionality reduction",
author = "J{\o}rgensen, {Dan Richter} and Dam, {Erik Bj{\o}rnager} and Martin Lillholm",
year = "2013",
doi = "10.1016/j.compbiomed.2013.05.012",
language = "English",
volume = "43",
pages = "1045--1052",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Pergamon Press",
number = "8",

}

RIS

TY - JOUR

T1 - Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps

AU - Jørgensen, Dan Richter

AU - Dam, Erik Bjørnager

AU - Lillholm, Martin

PY - 2013

Y1 - 2013

N2 - This study investigates whether measures of knee cartilage thickness can predict future loss of knee cartilage. A slow and a rapid progressor group was determined using longitudinal data, and anatomically aligned cartilage thickness maps were extracted from MRI at baseline. A novel machine learning framework was then trained using these maps. Compared to measures of mean cartilage plate thickness, group separation was increased by focusing on local cartilage differences. This result is central for clinical trials where inclusion of rapid progressors may help reduce the period needed to study effects of new disease-modifying drugs for osteoarthritis. (C) 2013 Elsevier Ltd. All rights reserved.

AB - This study investigates whether measures of knee cartilage thickness can predict future loss of knee cartilage. A slow and a rapid progressor group was determined using longitudinal data, and anatomically aligned cartilage thickness maps were extracted from MRI at baseline. A novel machine learning framework was then trained using these maps. Compared to measures of mean cartilage plate thickness, group separation was increased by focusing on local cartilage differences. This result is central for clinical trials where inclusion of rapid progressors may help reduce the period needed to study effects of new disease-modifying drugs for osteoarthritis. (C) 2013 Elsevier Ltd. All rights reserved.

KW - Osteoarthritis

KW - Knee MRI

KW - Cartilage thickness

KW - Clinical trials

KW - Machine learning

KW - Classification

KW - Spatial data mining

KW - Dimensionality reduction

U2 - 10.1016/j.compbiomed.2013.05.012

DO - 10.1016/j.compbiomed.2013.05.012

M3 - Journal article

C2 - 23773813

VL - 43

SP - 1045

EP - 1052

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

IS - 8

ER -

ID: 119703441