Applications of deep learning in biomedical engineering

Nisha, S. Shajun; Meeral, M. Nagoor

doi:10.1016/b978-0-12-823014-5.00008-9

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…A CNN model consists of an input layer, hidden layers, and an output layer. The hidden part comprises three main components, namely convolutional, pooling, and fully connected layers (Shajun Nisha & Nagoor Meeral, 2021). Using multiple linear and nonlinear operations, convolutional layers are able to extract the main features through which their corresponding spatial information can be preserved (Zhu et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions.

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Section: Methodsmentioning

confidence: 99%

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

Panahi,

Khosravi,

Rezaie

et al. 2023

Earth's Future

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“…Here we discuss how this work have been translated to the field of tissue engineering to resolve the challenges related to hiPSC-CM maturity. It is worth noting others have sought to review the applications of machine learning in stem cell therapies (Coronnello and Francipane, 2022), cardiovascular pathology and treatment (Bizopoulos and Koutsouris, 2019;Glass et al, 2022;Kresoja et al, 2023), 3D bioprinting (An et al, 2021), organ-on-a-chip methodologies (Koyilot et al, 2022), and biomedical engineering as a whole (Shajun Nisha and Nagoor Meeral, 2021), yet few focus on the specific challenges of CTE and provide an in-depth review of how machine learning may be leveraged to improve the maturation of hiPSC-CMs.…”

Section: Open Access Edited Bymentioning

confidence: 99%

A review on machine learning approaches in cardiac tissue engineering

Kalkunte,

Cisneros,

Castillo

et al. 2024

Front. Biomater. Sci.

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Cardiac tissue engineering (CTE) holds promise in addressing the clinical challenges posed by cardiovascular disease, the leading global cause of mortality. Human induced pluripotent stem cells (hiPSCs) are pivotal for cardiac regeneration therapy, offering an immunocompatible, high density cell source. However, hiPSC-derived cardiomyocytes (hiPSC-CMs) exhibit vital functional deficiencies that are not yet well understood, hindering their clinical deployment. We argue that machine learning (ML) can overcome these challenges, by improving the phenotyping and functionality of these cells via robust mathematical models and predictions. This review paper explores the transformative role of ML in advancing CTE, presenting a primer on relevant ML algorithms. We focus on how ML has recently addressed six key address six key challenges in CTE: cell differentiation, morphology, calcium handling and cell-cell coupling, contraction, and tissue assembly. The paper surveys common ML models, from tree-based and probabilistic to neural networks and deep learning, illustrating their applications to better understand hiPSC-CM behavior. While acknowledging the challenges associated with integrating ML, such as limited biomedical datasets, computational costs of learning data, and model interpretability and reliability, we examine suggestions for improvement, emphasizing the necessity for more extensive and diverse datasets that incorporate temporal and imaging data, augmented by synthetic generative models. By integrating ML with mathematical models and existing expert knowledge, we foresee a fruitful collaboration that unites innovative data-driven models with biophysics-informed models, effectively closing the gaps within CTE.

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“…The training time and memory requirements of SVM increase significantly as the size of the dataset grows, which can limit its scalability in certain applications. SVM has hyperparameters that need to be carefully tuned, such as the choice of kernel function and regularization parameter (Nisha and Meeral, 2021). The performance of SVM can be sensitive to the selection of these parameters, and finding the optimal combination can be a challenging task, requiring extensive experimentation and cross-validation.…”

Section: Support Vector Machinementioning

confidence: 99%

Trends in using deep learning algorithms in biomedical prediction systems

Wang,

Liu,

Wang

2023

Front. Neurosci.

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In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

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Applications of deep learning in biomedical engineering

Cited by 18 publications

References 11 publications

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

A Country Wide Evaluation of Sweden's Spatial Flood Modeling With Optimized Convolutional Neural Network Algorithms

A review on machine learning approaches in cardiac tissue engineering

Trends in using deep learning algorithms in biomedical prediction systems

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