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Sensors (Basel, Switzerland) Jun 2021Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning... (Review)
Review
Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), -nearest neighbors (-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.
Topics: Algorithms; Computers, Handheld; Machine Learning; Neural Networks, Computer; Support Vector Machine
PubMed: 34203119
DOI: 10.3390/s21134412 -
International Journal of Molecular... Jun 2022In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing,...
In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language processing, and now rapidly becoming a dominant tool in biomedicine [...].
Topics: Computational Biology; Deep Learning; Image Processing, Computer-Assisted; Machine Learning; Natural Language Processing
PubMed: 35743052
DOI: 10.3390/ijms23126610 -
Knee Surgery, Sports Traumatology,... Apr 2023Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or... (Review)
Review
Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on "big data" develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.
Topics: Humans; Supervised Machine Learning; Algorithms; Machine Learning; Orthopedic Procedures
PubMed: 36222893
DOI: 10.1007/s00167-022-07181-2 -
Methods in Molecular Biology (Clifton,... 2022Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the...
Machine learning (ML) already accelerates discoveries in many scientific fields and is the driver behind several new products. Recently, growing sample sizes enabled the use of ML approaches in larger omics studies. This work provides a guide through a typical analysis of an omics dataset using ML. As an example, this chapter demonstrates how to build a model predicting Drug-Induced Liver Injury based on transcriptomics data contained in the LINCS L1000 dataset. Each section covers best practices and pitfalls starting from data exploration and model training including hyperparameter search to validation and analysis of the final model. The code to reproduce the results is available at https://github.com/Evotec-Bioinformatics/ml-from-omics .
Topics: Chemical and Drug Induced Liver Injury; Humans; Machine Learning; Support Vector Machine
PubMed: 34731480
DOI: 10.1007/978-1-0716-1787-8_18 -
Neuroradiology Dec 2021Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology. (Review)
Review
PURPOSE
Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology.
METHODS
When designing AI-based research in neuroradiology and appreciating the literature, it is important to understand the fundamental principles of AI. Training, validation, and test datasets must be defined and set apart as priorities. External validation and testing datasets are preferable, when feasible. The specific type of learning process (supervised vs. unsupervised) and the machine learning model also require definition. Deep learning (DL) is an AI-based approach that is modelled on the structure of neurons of the brain; convolutional neural networks (CNN) are a commonly used example in neuroradiology.
RESULTS
Radiomics is a frequently used approach in which a multitude of imaging features are extracted from a region of interest and subsequently reduced and selected to convey diagnostic or prognostic information. Deep radiomics uses CNNs to directly extract features and obviate the need for predefined features.
CONCLUSION
Common limitations and pitfalls in AI-based research in neuroradiology are limited sample sizes ("small-n-large-p problem"), selection bias, as well as overfitting and underfitting.
Topics: Artificial Intelligence; Deep Learning; Humans; Machine Learning; Neural Networks, Computer; Prognosis
PubMed: 34537858
DOI: 10.1007/s00234-021-02813-9 -
Veterinary Radiology & Ultrasound : the... Dec 2022The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides... (Review)
Review
The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides essential definitions of AI with medical images with a focus on veterinary radiology. Machine learning methods common in medical image analysis are compared, and a detailed description of convolutional neural networks commonly used in deep learning classification and regression models is provided. A brief introduction to natural language processing (NLP) and its utility in machine learning is also provided. NLP can economize the creation of "truth-data" needed when training AI systems for both diagnostic radiology and radiation oncology applications. The goal of this publication is to provide veterinarians, veterinary radiologists, and radiation oncologists the necessary background needed to understand and comprehend AI-focused research projects and publications.
Topics: Animals; Humans; Artificial Intelligence; Deep Learning; Diagnostic Imaging; Machine Learning; Radiology
PubMed: 36514230
DOI: 10.1111/vru.13160 -
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.Methods (San Diego, Calif.) Aug 2020In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research... (Review)
Review
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
Topics: Artificial Intelligence; Deep Learning; Drug Discovery; Ligands; Machine Learning; Neural Networks, Computer; Receptors, G-Protein-Coupled; Software; Supervised Machine Learning
PubMed: 32645448
DOI: 10.1016/j.ymeth.2020.06.016 -
Methods in Molecular Biology (Clifton,... 2022The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and... (Review)
Review
The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.
Topics: Artificial Intelligence; Deep Learning; Drug Design; Ligands; Machine Learning
PubMed: 34731478
DOI: 10.1007/978-1-0716-1787-8_16 -
Journal of Cardiovascular Magnetic... Oct 2019Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where... (Review)
Review
Machine learning (ML) is making a dramatic impact on cardiovascular magnetic resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR where ML, and deep learning in particular, can assist clinicians and engineers in improving imaging efficiency, quality, image analysis and interpretation, as well as patient evaluation. We discuss recent developments in the field of ML relevant to CMR in the areas of image acquisition & reconstruction, image analysis, diagnostic evaluation and derivation of prognostic information. To date, the main impact of ML in CMR has been to significantly reduce the time required for image segmentation and analysis. Accurate and reproducible fully automated quantification of left and right ventricular mass and volume is now available in commercial products. Active research areas include reduction of image acquisition and reconstruction time, improving spatial and temporal resolution, and analysis of perfusion and myocardial mapping. Although large cohort studies are providing valuable data sets for ML training, care must be taken in extending applications to specific patient groups. Since ML algorithms can fail in unpredictable ways, it is important to mitigate this by open source publication of computational processes and datasets. Furthermore, controlled trials are needed to evaluate methods across multiple centers and patient groups.
Topics: Cardiovascular Diseases; Coronary Circulation; Deep Learning; Diagnosis, Computer-Assisted; Humans; Image Interpretation, Computer-Assisted; Machine Learning; Magnetic Resonance Imaging, Cine; Myocardial Perfusion Imaging; Myocardium; Predictive Value of Tests; Reproducibility of Results; Supervised Machine Learning; Unsupervised Machine Learning
PubMed: 31590664
DOI: 10.1186/s12968-019-0575-y -
Journal of Clinical Ultrasound : JCU Nov 2022Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest... (Review)
Review
Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.
Topics: Humans; Artificial Intelligence; Deep Learning; Machine Learning; Image Processing, Computer-Assisted; Musculoskeletal System
PubMed: 36069404
DOI: 10.1002/jcu.23321