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Performance assessment of AI tools for digitizing ECG scans

Adamopoulos, Ioannis
Master thesis
Published version
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URI
https://hdl.handle.net/11250/3015944
Date
2022
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  • TKD - Master i Anvendt data- og informasjonsteknologi (ACIT) [237]
Abstract
The digitization of electrocardiogram (ECG) signals recorded on paper is a very

challenging task usually prone to errors and inaccuracies. Until now there is no

available tool which can perform that task both universally and in a fully automated

way. ECG is a well established medical modality to record the activity of the human

heart which dates to over 100 years ago. Medical experts can diagnose potential heart

irregularities by interpreting the recorded signals on ECG papers. There are millions of

ECGs worldwide and the digitization of them is of paramount importance for research,

analysis and diagnosis in medicine. By recognizing patterns in the digitized ECGs,

artificial intelligence algorithms can predict cardiovascular diseases and help clinicians

to make better medical diagnoses. Therefore, the development or the improvement

of an automated tool that will be able to digitize massively ECG signals at once, is

essential. In this thesis we describe and develop a tool for digitizing ECGs and we

test its performance using ECG scans provided by Akershus University Hospital. In

particular, we introduce some improvements which can make it operate in a more

automated way. The original tool has several parameters in its various steps that

prevent it from being fully automated. However, with proper further improvements,

it has a great potential to become fully automated at least for ECG scans similar to

the ones in the database of Akershus University Hospital. The current master thesis

was held during the last semester of the ACIT’s master program of Oslo Metropolitan

University, namely from January 1st until May 15th.
Publisher
OsloMet - storbyuniversitetet
Series
ACIT;2022

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