Information
- Publication Type: Master Thesis
- Workgroup(s)/Project(s): not specified
- Date: October 2022
- Date (Start): 2021
- Date (End): 2022
- Diploma Examination: 10. October 2022
- First Supervisor: Renata Raidou
- Pages: 148
- Keywords: Convolutional Neural Network, Prediction, Knee Osteoarthritis
Abstract
Osteoarthritis (OA) is a slowly degenerative joint disease, with cartilage loss as one of the most characteristic symptoms accompanied by pain and functional disability. The knee region is the most affected area. 22.9% of the worldwide population over the age of 40 were affected in 2020 by Knee Osteoarthritis (KOA). Besides normal KOA, which develops over multiple years, the accelerated form of KOA (AKOA) develops between 1 and 4 years and is accompanied by increased pain and movement restrictions as well as a higher chance of obtaining a knee replacement. The development of AKOA is not yet predictable on the basis of a single X-ray image because there is no apparent optical difference between the baseline X-ray of KOA and AKOA. Since Convolutional Neural Networks (CNN) can identify image structures that a human eye can not see, I want to realise an early diagnosis of AKOA by using a Convolutional Neural Network (CNN) as a classifier between slow- and fast-progressing KOA.For this purpose, I used the data from three different studies, including a knee X-ray, Body Mass Index (BMI), age, gender, Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores, hip symptoms, knee medication injection and Kellgren- Lawrence (KL)-grade, as input for binary classification models. I defined AKOA once with Joint Space Narrowing (JSN) > 10%/ 2 years and once with JSN > 20%/ 2 years and performed different experiments in order to find the best method to predict AKOA. I trained the numeric data only on an Extreme Gradient Boosting (XGBoost) model. Here I achieved the highest performance of an Area Under the Curve (AUC) of 0.6616 when including the Osteoarthritis Research Society International (OARSI) score of sclerosis and osteophytosis to the numeric input data (20% JSN/ 2 years). To use image data only and the combination of both, I created different CNN models, whose architecture is based on a Residual Network (ResNet) 50 model provided by Image Biopsy Lab (IBLab). The CNN model, which I trained only with image data, yielded an AUC of 56.26% (10% JSN/ 2 years). Using the image data complemented with the most important numeric features (gender, BMI, contralateral KOA, KL-grade) as input, I achieved an AUC of 68.78% (20% JSN/ 2 years). Comparable results, but obtained with other class definitions than in this work, were higher and yielded AUCs of around 0.8.These results show that it is possible to make a risk assessment about the development of AKOA using the baseline X-ray image, gender, BMI, the KL-grade and the information about contralateral KOA. Until now, radiologists are not capable of predicting fast-progressing KOA. Hence, these networks have a great potential to be used as AKOA prediction tools.
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Weblinks
BibTeX
@mastersthesis{vogel2022,
title = "Prediction of Accelerated Knee Osteoarthritis Using a
Convolutional Neural Network",
author = "Magdalena Vogel",
year = "2022",
abstract = "Osteoarthritis (OA) is a slowly degenerative joint disease,
with cartilage loss as one of the most characteristic
symptoms accompanied by pain and functional disability. The
knee region is the most affected area. 22.9% of the
worldwide population over the age of 40 were affected in
2020 by Knee Osteoarthritis (KOA). Besides normal KOA, which
develops over multiple years, the accelerated form of KOA
(AKOA) develops between 1 and 4 years and is accompanied by
increased pain and movement restrictions as well as a higher
chance of obtaining a knee replacement. The development of
AKOA is not yet predictable on the basis of a single X-ray
image because there is no apparent optical difference
between the baseline X-ray of KOA and AKOA. Since
Convolutional Neural Networks (CNN) can identify image
structures that a human eye can not see, I want to realise
an early diagnosis of AKOA by using a Convolutional Neural
Network (CNN) as a classifier between slow- and
fast-progressing KOA.For this purpose, I used the data from
three different studies, including a knee X-ray, Body Mass
Index (BMI), age, gender, Western Ontario and McMaster
Universities Arthritis Index (WOMAC) scores, hip symptoms,
knee medication injection and Kellgren- Lawrence (KL)-grade,
as input for binary classification models. I defined AKOA
once with Joint Space Narrowing (JSN) > 10%/ 2 years and
once with JSN > 20%/ 2 years and performed different
experiments in order to find the best method to predict
AKOA. I trained the numeric data only on an Extreme Gradient
Boosting (XGBoost) model. Here I achieved the highest
performance of an Area Under the Curve (AUC) of 0.6616 when
including the Osteoarthritis Research Society International
(OARSI) score of sclerosis and osteophytosis to the numeric
input data (20% JSN/ 2 years). To use image data only and
the combination of both, I created different CNN models,
whose architecture is based on a Residual Network (ResNet)
50 model provided by Image Biopsy Lab (IBLab). The CNN
model, which I trained only with image data, yielded an AUC
of 56.26% (10% JSN/ 2 years). Using the image data
complemented with the most important numeric features
(gender, BMI, contralateral KOA, KL-grade) as input, I
achieved an AUC of 68.78% (20% JSN/ 2 years). Comparable
results, but obtained with other class definitions than in
this work, were higher and yielded AUCs of around 0.8.These
results show that it is possible to make a risk assessment
about the development of AKOA using the baseline X-ray
image, gender, BMI, the KL-grade and the information about
contralateral KOA. Until now, radiologists are not capable
of predicting fast-progressing KOA. Hence, these networks
have a great potential to be used as AKOA prediction tools.",
month = oct,
pages = "148",
address = "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
school = "Research Unit of Computer Graphics, Institute of Visual
Computing and Human-Centered Technology, Faculty of
Informatics, TU Wien",
keywords = "Convolutional Neural Network, Prediction, Knee
Osteoarthritis",
URL = "https://www.cg.tuwien.ac.at/research/publications/2022/vogel2022/",
}
Master thesis
