Analyzing right ventricular attributes with machine learning: A deep learning solution trained on model-annotated data
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Hagerman Olzon, David
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
An accurate analysis of echocardiograms is key to diagnosing heart conditions in
modern medicine. This analysis is today performed by physicians and requires expertise
that might only come after years of training in the area. In this master
thesis we explore the possibility of using machine learning models, trained on large
amounts of echocardigrams, that when trained can predict the mobility and size of
the right ventricle. A successful model could be useful in assisting both inexperienced
physicians during training or as a tool to speed up analysis for the already
trained experts.
To train a supervised model, large amounts of annotated data is required but annotating
the echocardiograms requires high expertise and is time consuming. As
a possible solution to this problem, a separate text classifier trained on transcript
data were used to annotate the echocardiograms that were linked to the respective
examination. The transcript data consisted of a physicians analysis on all examination
data that belonged to a patient, including the echocardiograms.
Several different architectures for the text classifier were trained and evaluated and
the best performing model achived 92% accuracy on classifying the mobility and
95% accuracy at classifying the size of the right ventricle using transcript data. The
trained text models were then used to annotate the echocardiograms in the dataset
and the resulting data were then was used to train a set of image classifiers. The
echocardiograms are 3-dimensional data and our results showed that models using a
3-dimensional representation also performed the best on the two classification tasks.
The best models used a combination of human-annotated and model-annotated data
and achieved 82% and 83% accuracy on mobility and size respectively. This result
can be compared to the interobserver agreements between our transcript analysis
and two experts annotating echocardiograms from the test set which were 82% for
mobility and 72% for size.
The study showed that the use of a machine learning model as tool for physicians
is a feasible and an interesting prospect. As a continuation of the work done here,
the first step would be to increase the size of the models and training data. With
the help of the automatic annotation, additional data is relatively easy to process.
Other ways forward would be to include additional parameters from the examinations
such as heart rate and to analyze the right ventricle attributes in relation to
each other.
Beskrivning
Ämne/nyckelord
machine learning , ai , bert , cnn , classification , echocardiography , resnext , ultrasound , deep learning