Geometrical evaluation of the Scar in Left ventricle using TDA

Singh, Yashbir; Deepa, S.; Mansoor, Wathiq; Hu, Wei-Chih

doi:10.1109/ccwc51732.2021.9376058

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To measure cardiac fats, a couple of imaging modalities such as magnetic resonance imaging (MRI), echocardiography, and computed tomography (CT) are currently accessible in the market. However, detection of the fat can need either manual strategies which can be tedious or using some available strategies [14][15][16][17]. TDA combines algebraic topology and statistical learning techniques to provide a mathematical foundation for studying the shape of data.…”

Section: Resultsmentioning

confidence: 99%

Comparative study of left atrium epicardial fat tissue pattern using persistent homology approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective Atrial Fibrillation (A-fib) is an abnormal heartbeat condition in which the heart races and beats in an uncontrollable way. It is observed that the presence of increased epicardial fat/fatty tissue in the atrium can lead to A-fib. Persistent homology using topological features can be used to recapitulate enormous amounts of spatially complicated medical data into a visual code to identify a specific pattern of epicardial fat tissue with non-fat tissue. Our aim is to evaluate the topological pattern of left atrium epicardial fat tissue with non-fat tissue. Results A topological data analysis approach was acquired to study the imaging pattern between the left atrium epicardial fat tissue and non-fat tissue patches. The patches of eight patients from CT images of the left atrium heart were used and categorized into “left atrium epicardial fat tissue” and “non-fat tissue” groups. The features that distinguish the “epicardial fat tissue” and “non-fat tissue” groups are extracted using persistent homology (PH). Our result reveals that our proposed research can discriminate between left atrium epicardial fat tissue and non-fat tissue. Specifically, the range of Betti numbers in the epicardial tissue is smaller (0–30) than the non-fat tissue (0–100), indicating that non-fat tissue has good topology.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparative study of left atrium epicardial fat tissue pattern using persistent homology approach

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The extracted features and data from images can be feed in machine learning algorithms to detect and differentiate between physiological versus pathological abnormality. However, the segmentation technique of pixel-based classification of a structure can be very challenging when the boundaries are missing or when ultrasound data is of poor quality [4]. ML analysis of combining clinical variable data with echocardiography images can also, provide the bulk of clinically useful information as well as help to identify unrecognized associations between risk factors and relevant cardiac diseases.…”

Section: Imaging Modalities For Cardiac Imagingmentioning

confidence: 99%

Integration of Machine Learning into the Field of Cardiac Imaging

Mishra¹,

Hu²

2022

Act Scie Medic

Self Cite

View full text Add to dashboard Cite

Machine learning (ML) has changed an essential aspect of human life. This is a subdivision of Artificial intelligence where the machines automatically extract valuable information by the databases' patterns. It has been widely used in medical science, and particularly within the area of computational cardiology. Here, in this chapter, we present a brief picture of a machine-learning algorithm that is used for predictive data-driven models. We also emphasize various domains of machine learning application, such as non-invasive imaging modalities. We bring to a close-by reviewing the drawbacks associated with the current application of Machine learning algorithms within computational cardiology.

show abstract