Radon descriptor-based machine learning using CT images to predict the fat tissue on left atrium in the heart.
Proc Inst Mech Eng H
; 236(8): 1232-1237, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-35791086
ABSTRACT
Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one's life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine learning approach, the patches of eight patients from CT images of the heart were used and categorized into "epicardial fat tissue" and "nonfat tissue" groups. The 10 feature vectors are extracted from each big patch using Radon descriptors and then fed into a traditional machine learning model. The results show that the proposed methodology discriminates between fat tissues and nonfat tissues clearly. KNN has shown the best performance with 96.77% specificity, 98.28% sensitivity, and 97.50% accuracy. To our knowledge, this study is the first attempt to provide a Radon transform-based machine learning method to distinguish between fat tissue and nonfat tissue on the left atrium. Our proposed research method could be potentially used in advanced interventions.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fibrilação Atrial
/
Radônio
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
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Screening_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article