Comparative Analysis of the Results of Algorithms for Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy Using Deep Learning

Nasimova, Nigorakhon; Muminov, Bahodir; Nasimov, Rashid; Abdurashidova, Kamola; Abdullaev, Munis

doi:10.1109/icisct52966.2021.9670134

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…Furthermore, implementation strategies are needed to improve HCM recognition and distinguish between HCM and hypertension in community settings that may not have the resources to regularly utilize cardiac magnetic resonance. Recent advances in artificial intelligence and machine learning in detecting HCM via electrocardiogram or echocardiogram, both more accessible than CMR, have shown promise in accurately detecting HCM [ [37] , [38] , [39] ].…”

Section: Discussionmentioning

confidence: 99%

Clinical course of adults with co-occurring hypertrophic cardiomyopathy and hypertension: A scoping review

Arabadjian,

Montgomery,

Pleasure

et al. 2024

American Heart Journal Plus: Cardiology Research and Practice

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Section: Discussionmentioning

confidence: 99%

Clinical course of adults with co-occurring hypertrophic cardiomyopathy and hypertension: A scoping review

Arabadjian,

Montgomery,

Pleasure

et al. 2024

American Heart Journal Plus: Cardiology Research and Practice

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“…GANs demonstrated superior performance than traditional CNNs with limited data, attaining a test accuracy of 92.3%. Nasimova et al [ 140 ] introduced a deep convolutional neural network for classifying echocardiography videos as Dilated Cardiomyopathy or Hypertrophic Cardiomyopathy. Their study initially generated an Echo dataset from internet-sourced Echo videos and EchoNet database videos.…”

Section: Reported Workmentioning

confidence: 99%

“…The core technologies and algorithms of AI models designed for different diseases are typically general. For instance, a Convolutional Neural Network (CNN) has been widely applied in the diagnosis of AD [ 80 ], breast cancer [ 96 ], depression [ 121 ], heart disease [ 140 ], and epilepsy [ 158 ]. However, deploying AI models developed for specific diseases to other disease predictions often demonstrates limited generalization ability.…”

Section: Challenges and Future Workmentioning

confidence: 99%

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu,

Li,

Zhu

et al. 2024

Bioengineering

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Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

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“…In the last few years, the use of artificial intelligence (AI) to analyze images, videos, text, and audio, in order to interpret, detect, classify, and diagnose diseases, has attracted the growing interest of researchers [ 1 , 2 ]. The development of medical AI-based software requires a huge amount of data such as blood test results, X-rays, Computed tomography (CT), Magnetic resonance imaging (MRI) Echocardiography (Echo) images, etc.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

Abdusalomov

Nasimov

Nasimova

et al. 2023

Sensors

Self Cite

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In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set. Then, the proximity of synthetic images to the real set is evaluated using different distance metrics. However, it is not possible to determine whether only one synthetic image was generated repeatedly, or whether the synthetic set exactly repeats the training set. In addition, most evolution metrics take a lot of time to calculate. Taking these issues into account, we have proposed a method that can quantitatively and qualitatively evaluate synthetic images. This method is a combination of two methods, namely, FMD and CNN-based evaluation methods. The estimation methods were compared with the FID method, and it was found that the FMD method has a great advantage in terms of speed, while the CNN method has the ability to estimate more accurately. To evaluate the reliability of the methods, a dataset of different real images was checked.

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Comparative Analysis of the Results of Algorithms for Dilated Cardiomyopathy and Hypertrophic Cardiomyopathy Using Deep Learning

Cited by 12 publications

References 21 publications

Clinical course of adults with co-occurring hypertrophic cardiomyopathy and hypertension: A scoping review

Clinical course of adults with co-occurring hypertrophic cardiomyopathy and hypertension: A scoping review

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm

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