Results - Details
Search command : Author="Τσέτης"
And Author="Δημήτριος"
Current Record: 2 of 17
|
Identifier |
000455955 |
Title |
Artificial intelligence in bone marrow imaging : Development of novel machine learning strategies for the diagnosis and classification of benign bone marrow pathology with the use of magnetic resonance imaging (MRI) |
Alternative Title |
Τεχνητή νοημοσύνη στην απεικόνηση του οστικού μυελού |
Author
|
Κλώντζας, Μιχαήλ
|
Thesis advisor
|
Καραντάνας Απόστολος
|
Reviewer
|
Μαριάς, Κώστας
Ζιμπής, Αριστείδης
Τσέτης, Δημήτριος
Περυσινάκης, Κωνσταντίνος
Φωτιάδης, Δημήτριος
Κρανιώτη, Ελενα
|
Abstract |
Bone marrow edema (BME) is a non-specific finding that can accompany a wide
variety of conditions affecting the bone marrow including acute trauma, acute bone
marrow edema syndromes (transient osteoporosis, regional migratory osteoporosis),
chronic regional pain syndrome, avascular necrosis, infection, inflammatory arthritis,
osteoarthritis at advanced stages of the disease, tendinopathies, and primary and
metastatic malignancies. The imaging modality of choice for the depiction of bone
marrow edema is magnetic resonance imaging (MRI) with fluid-sensitive sequences.
The appearance of BME on MRI can complicate the diagnosis of diseases affecting the
bone marrow, creating diagnostic dilemmas that challenge general radiologists or
even specialized musculoskeletal radiologists. The most important diagnostic
challenges faced in everyday radiological practice are (a) the differentiation between
transient osteoporosis of the hip and avascular necrosis, (b) the differentiation
between subchondral insufficiency fractures of the knee and advanced osteoarthritis
presenting with BME and (c) the accurate staging of avascular necrosis. Accurate
diagnosis in these cases is of utmost importance since it can change the treatment from
conservative (for transient osteoporosis) to surgical (for avascular necrosis) or can
determine the choice between joint preserving surgery (early stages of avascular
necrosis) and total hip arthroplasty (late stages of avascular necrosis. The aim of this
PhD was to leverage the power of novel image analysis methods such as radiomics
and deep learning to tackle the aforementioned diagnostic dilemmas. Radiomics
includes the extraction of high-dimensional data from regions of interest that can be
used for the detailed characterization of lesions. Artificial intelligence (traditional
machine learning or deep learning) methods can be used to either analyse radiomics
data or to independently perform image recognition tasks for diagnostic purposes
attempting the automation of disease detection. For the purposes of this PhD,
radiomics data have been utilized for the analysis of proximal femurs with either
avascular necrosis or transient osteoporosis of the hip, used to train machine learning
models to distinguish between the two conditions. These models achieved excellent
performance in distinguishing between the two conditions, performing equally to
musculoskeletal radiologists and better than a general radiologist. In addition, in
4
order to further automate the diagnosis between these two conditions, and to avoid
bias related to the manual steps for radiomics data preparation, deep learning was
used to distinguish between the two using whole images. Three convolutional neural
networks (CNNs) were trained with a transfer learning methodology and finetuned
with our data, in order to diagnose between transient osteoporosis and avascular
necrosis. The consensus decision between the three CNNs was found to be highly
accurate, performing better than two experts. Subsequently, a CNN ensemble was
used to differentiate between subchondral insufficiency fractures and advanced
osteoarthritis of the knee. The consensus decision of the network ensemble was
compared to the diagnosis of expert radiologists. This CNN ensemble was found to
be highly accurate in the differentiation between the two conditions performing better
than one of the two experts. Finally, CNNs were used to distinguish between early
(ARCO 1-2) and late stages of avascular necrosis (ARCO 3-4). The consensus decision
of three CNNs was found to reach high performance in this diagnostic task. To further
validate the model, a dataset from another country was used to assess model
performance on unknown data and this validation performance was compared to the
diagnosis of expert readers. Despite the performance drop in the external dataset, the
CNN ensemble was still highly accurate in recognizing late AVN achieving a
performance similar to the two experts. In conclusion, the work presented herein
demonstrated the potential of radiomics and deep learning to assist diagnostic
decisions in some of the most complicated tasks related to the presence of BME on
MRI.
|
Language |
English |
Subject |
A vascular necrosis |
|
Deep learning |
|
Radiomics |
|
Transient osteoporosis |
|
Ακτινολογία |
|
Μαγνητική τομογραφία |
|
Οστεονέκρωση |
|
Οστικός μυελός |
|
Παροδική οστεοπόρωση |
|
Ραδιωμική |
Issue date |
2023-07-28 |
Collection
|
School/Department--School of Medicine--Department of Medicine--Doctoral theses
|
|
Type of Work--Doctoral theses
|
Permanent Link |
https://elocus.lib.uoc.gr//dlib/6/7/9/metadata-dlib-1687170047-425630-24736.tkl
|
Views |
816 |