Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Glavaški, Mila; Preveden, Andrej; Jakovljević, Đorđe; Filipović, Nenad; Velicki, Lazar

doi:10.3390/life12101566

Cited by 4 publications

(3 citation statements)

References 57 publications

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“…The systolic pulmonary arterial pressure was estimated using the maximal TR jet velocity and the inferior vena cava respiratory variation. [12][13][14][15][16]. In this paper, a novel deep learning technique called Tricuspid Regurgitation Identification in Fetal Heart (TRI-FH) approach has been proposed, for identifying TR in the early stages.…”

Section: Introductionmentioning

confidence: 99%

Fetal 3d-Echo Classification and Segmentation Using Color and Textural Features for Tr Detection

PRIYA,

RAMAKRISHNAN,

KARTHIK

2024

RRST-EE

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Tricuspid regurgitation (TR) is a condition in which the valve between the right atrium and right ventricle does not close properly. Therefore, blood leaks backward into the upper right chamber. Most commonly, tricuspid regurgitation is caused by an enlarged right ventricle. In this paper, a novel deep learning technique called Tricuspid regurgitation identification in the fetal heart (TRI-FH) approach has been proposed, for identifying TR in the early stages. The gathered 3D-echo images are pre-processed to improve the quality of the images. The feature extraction techniques namely color-level co-occurrence matrix (CLCM) and grey-level co-occurrence matrix (GLCM) are applied on both RGB and grey images. The extracted features are extracted using the wrapper method and the unsupervised dimension reduction technique namely independent component analysis (ICA) is used to reduce the dimension of extracted features. Afterward, the deep learning-based Ghost network is used for classifying normal, Mild TR, and severe TR cases. The classified TR cases are fed to the segmentation phase for segmenting the affected valve of the fetus. The experimental TRI-FH approach achieves a total accuracy of 98.07 %. As compared to existing techniques, the proposed TRI-FH method shows higher performance in terms of accuracy, precision, recall, specificity, and F1 score. The proposed TRI-FH model enhances the total accuracy by 95.64 %, 97.82 %, 93.21 %, and 95.67 % better than ResNet, DenseNet, LinkNet, and U-Net respectively.

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Section: Introductionmentioning

confidence: 99%

Fetal 3d-Echo Classification and Segmentation Using Color and Textural Features for Tr Detection

PRIYA,

RAMAKRISHNAN,

KARTHIK

2024

RRST-EE

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“…And its subtypes. It is important to note that there is no “one-size-fits-all” clustering algorithm and they can be used based on the research requirements considering their respective merits and limitations ( Glavaški et al, 2022 )( Nayak & Sundararajan, 2023 ). The linear interaction energy (LIE) analysis computes the non-bonded van der Waals (vdW) and electrostatic (ele) interactions for the compound with protein target and the compound in water to compare the bound and unbound state of the small molecule/ligand/compound as well as binding capacity of the compound.…”

Section: Introductionmentioning

confidence: 99%

Advances in computational frameworks in the fight against TB: The way forward

Naidu

Nayak

et al. 2023

Front. Pharmacol.

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Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its “End TB” strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for—early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

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“…Artificial intelligence (AI) is used to study patterns in data and develop models that can make predictions. Explainable artificial intelligence methods demonstrate the relationships among such predictions [36]. Applications of AI have the potential to be useful across several medical domains [37].…”

Section: Introductionmentioning

confidence: 99%

Gene-Specific Discriminative Echocardiogram Findings in Hypertrophic Cardiomyopathy Determined Using Artificial Intelligence: A Pilot Study

Glavaški,

Ilić,

Velicki

2023

Cardiogenetics

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Hypertrophic cardiomyopathy (HCM) is among the most common forms of cardiomyopathies, with a prevalence of 1:200 to 1:500 people. HCM is caused by variants in genes encoding cardiac sarcomeric proteins, of which a majority reside in MYH7, MYBPC3, and TNNT2. Up to 40% of the HCM cases do not have any known HCM variant. Genotype–phenotype associations in HCM remain incompletely understood. This study involved two visits of 46 adult patients with a confirmed diagnosis of HCM. In total, 174 genes were analyzed on the Next-Generation Sequencing platform, and transthoracic echocardiography was performed. Gene-specific discriminative echocardiogram findings were identified using the computer vision library Fast AI. This was accomplished with the generation of deep learning models for the classification of ultrasonic images based on the underlying genotype and a later analysis of the most decisive image regions. Gene-specific echocardiogram findings were identified: for variants in the MYH7 gene (vs. variant not detected), the most discriminative structures were the septum, left ventricular outflow tract (LVOT) segment, anterior wall, apex, right ventricle, and mitral apparatus; for variants in MYBPC3 gene (vs. variant not detected) these were the septum, left ventricle, and left ventricle/chamber; while for variants in the TNNT2 gene (vs. variant not detected), the most discriminative structures were the septum and right ventricle.

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Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Cited by 4 publications

References 57 publications

Fetal 3d-Echo Classification and Segmentation Using Color and Textural Features for Tr Detection

Fetal 3d-Echo Classification and Segmentation Using Color and Textural Features for Tr Detection

Advances in computational frameworks in the fight against TB: The way forward

Gene-Specific Discriminative Echocardiogram Findings in Hypertrophic Cardiomyopathy Determined Using Artificial Intelligence: A Pilot Study

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