Precisión diagnóstica mejorada para la imagen de perfusión miocárdica usando redes neuronales artificiales en diferentes variables de entrada incluyendo datos clínicos y de cuantificación

Rahmani, Reza; Niazi, P.; Naseri, Maryam; Neishabouri, M.; Farzanefar, Saeed; Eftekhari, Mohammad; Derakhshan, Farhang; Mollazadeh, Reza; Meysami, Ali Pasha; Abbasi, Mehrshad

doi:10.1016/j.remn.2019.04.002

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Furthermore, LogitBoost with both clinical and imaging data (ML-Combined) was compared against the utilization of only imaging variables as input, and visual diagnosis and automated quantitative imaging analysis, and ML-combined outperformed with an AUC of 0.81. Rahmani et al [ 41 ] aimed to investigate the integration of ANN to predict obstructive CAD by adding clinical data. Ninety-three polar maps were included, with the patients in stress and rest demonstrations.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

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Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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“…In the study by Rahmani et al . [39]. , the authors demonstrated an ANN approach to predict CAD successfully.…”

Section: Resultsmentioning

confidence: 99%

“…In the study by Rahmani et al [39]., the authors demonstrated an ANN approach to predict CAD successfully. A total of 93 patients were included in stress and rest examination.…”

Section: Related Work In Diagnosis/classificationmentioning

confidence: 99%

AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review

Παπανδριανός

Apostolopoulos

Feleki

et al. 2022

Nuclear Medicine Communications

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In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image analysis. Deep learning has recently displayed remarkable applicability in a range of different medical applications, as well as in nuclear cardiology. This paper implements a literature review protocol and reports the latest advances in artificial intelligence (AI)-based classification in SPECT myocardial perfusion imaging in heart disease diagnosis. The representative and most recent works are reported to demonstrate the use of AI and deep learning technologies in medical image analysis in nuclear cardiology for cardiovascular diagnosis. This review also analyses the primary outcomes of the presented research studies and suggests future directions focusing on the explainability of the deployed deep-learning systems in clinical practice.

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“…Working with the analysis of images in myocardium samples provides quantitative data. Using machine vision, in this work they can classify the results and even achieve a prediction, for example, with artificial neural networks (ANN) (see [9]) and decision trees.…”

Section: State Of Art In Electrophoresis and Image Retrievalmentioning

confidence: 99%

Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel

et al. 2020

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Electrophoresis allows us to identify the types of proteins present in food, DNA, tissues and more. With the help of the molecular marker their weight is known, these markers are applied within the one-dimensional gel, and their protein value is known by means of marks. In this research, the molecular marker is obtained and the wavelet transform (WT) is obtained, generating approximation coefficients, which were taken to determine a molecular weight using three classification paradigms. The first paradigm is an approach in content-based image retrieval (CBIR) which makes a detection of the molecular weight in electrophoresis samples. The second approach is a neural network, thus two models are employed: self-organization maps (SOM) and back propagation in a supervised and unsupervised way, respectively. The third approach is based in a J48 decision tree. A comparison is made between the three paradigms for molecular weight computation. Neural networks obtained an improvement in the precision compared versus the CBIR-WT. Five parametric statistics were generated from the wavelet approximation coefficients. The CBIR-WT, SOM, back propagation and J48 were decisive for the classification and calculation of the molecular weight of each protein stain in the one-dimensional electrophoresis gel.

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Precisión diagnóstica mejorada para la imagen de perfusión miocárdica usando redes neuronales artificiales en diferentes variables de entrada incluyendo datos clínicos y de cuantificación

Cited by 5 publications

References 23 publications

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review

Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel

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