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A recent report from researchers at Birmingham Women's and Children's NHS Foundation Trust discusses the diagnosis and treatment of progressive familial intrahepatic cholestasis (PFIC), a rare liver disorder that primarily affects children. The researchers aimed to provide recommendations for the management of PFIC in clinical practice. They developed an algorithm for the diagnosis and treatment of children with suspected PFIC, which includes the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment. The authors hope that these recommendations will help standardize the management of PFIC and raise awareness of current developments in the field. [Extracted from the article]

The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

Keywords: Acid-Base Imbalance; Algorithms; Computers; Diabetes Complications; Diabetes Mellitus; Diabetic Ketoacidosis; Glucose Metabolism Disorders; Health and Medicine; Hospitals; Nutritional and Metabolic Diseases and Conditions; Peptide Hormones; Peptide Proteins; Proinsulin EN Acid-Base Imbalance Algorithms Computers Diabetes Complications Diabetes Mellitus Diabetic Ketoacidosis Glucose Metabolism Disorders Health and Medicine Hospitals Nutritional and Metabolic Diseases and Conditions Peptide Hormones Peptide Proteins Proinsulin 53 53 1 04/17/23 20230420 NES 230420 2023 APR 17 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- New study results on diabetic ketoacidosis have been published. Acid-Base Imbalance, Algorithms, Computers, Diabetes Complications, Diabetes Mellitus, Diabetic Ketoacidosis, Glucose Metabolism Disorders, Health and Medicine, Hospitals, Nutritional and Metabolic Diseases and Conditions, Peptide Hormones, Peptide Proteins, Proinsulin. [Extracted from the article]

A study conducted by researchers at Sichuan University of Science and Engineering in China explores the design of smart senior care (SSC) product app interfaces for the elderly. The researchers use deep learning (DL) technology to optimize the app interface design, taking into consideration the cognitive characteristics and habits of the elderly. The study finds that the proposed model based on the deep-Q-network (DQN) algorithm significantly improves the user experience and satisfaction of the elderly, enhancing their ability to utilize smart products and improve their quality of life. The research provides valuable insights for further research and practice in related fields. [Extracted from the article]

This article discusses the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in cancer research. It highlights how these technologies are transforming the diagnosis, treatment, and prevention of cancer by providing sophisticated analytical capabilities. The paper explores the mathematical foundations and practical implementations of AI and ML techniques in various aspects of cancer research, including detection, diagnosis, prognosis, and personalized treatment strategies. It also reviews influential algorithms such as Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), and clustering techniques, and their roles in improving diagnostic accuracy and treatment efficacy. Please note that this preprint has not been peer-reviewed. [Extracted from the article]

A report from the University of Oslo discusses the use of machine learning algorithms and heuristic stopping criteria in systematic reviews in education and educational psychology. The study found that using machine learning algorithms for abstract screening reduced the workload of irrelevant records by an average of 58% and saved an estimated 1.66 days of time. The random forests algorithm with sentence bidirectional encoder representations from transformers performed the best. The study also found that heuristic stopping rules could retrieve 95% of relevant abstracts, with the best results achieved by stopping the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers. The research provides empirical evidence on the performance of machine learning screening algorithms in this field. [Extracted from the article]

Algorithms, Diagnostics and Screening, Emerging Technologies, Fibromyalgia, Health and Medicine, Linear Discriminant Analysis, Machine Learning, Muscular Diseases and Conditions, Musculoskeletal Diseases and Conditions, Neuromuscular Diseases and Conditions, Rheumatic Diseases and Conditions Keywords: Algorithms; Diagnostics and Screening; Emerging Technologies; Fibromyalgia; Health and Medicine; Linear Discriminant Analysis; Machine Learning; Muscular Diseases and Conditions; Musculoskeletal Diseases and Conditions; Neuromuscular Diseases and Conditions; Rheumatic Diseases and Conditions EN Algorithms Diagnostics and Screening Emerging Technologies Fibromyalgia Health and Medicine Linear Discriminant Analysis Machine Learning Muscular Diseases and Conditions Musculoskeletal Diseases and Conditions Neuromuscular Diseases and Conditions Rheumatic Diseases and Conditions 158 158 1 04/10/23 20230413 NES 230413 2023 APR 13 (NewsRx) -- By a News Reporter-Staff News Editor at Hematology Week -- Research findings on fibromyalgia are discussed in a new report. [Extracted from the article]

A recent study conducted by researchers at Hamad Bin Khalifa University in Doha, Qatar, focuses on the use of machine learning and digital signal processing to improve the analysis of electroencephalogram (EEG) signals for medical diagnosis. EEG is a safe and common test that records electrical signals from the brain and has various applications in diagnosing conditions such as epilepsy, Alzheimer's, brain tumors, sleep disorders, and stroke. The study provides a comprehensive review of recent approaches for EEG preprocessing, feature extraction, and diagnosis of brain disorders, aiming to identify reliable algorithms for improving the accuracy of automatic diagnostic systems. The research concludes that the paper can be a valuable resource for researchers and practitioners in the field, offering insights into recent developments and future research directions. [Extracted from the article]

Keywords: Chicago; State:Illinois; United States; North and Central America; Algorithms; Cyborgs; Emerging Technologies; Health and Medicine; Machine Learning EN Chicago State:Illinois United States North and Central America Algorithms Cyborgs Emerging Technologies Health and Medicine Machine Learning 380 380 1 05/22/23 20230526 NES 230526 2023 MAY 28 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Fresh data on Machine Learning are presented in a new report. Machine learning algorithms may be used to compare the risk of failure of specific patient-procedure combinations in the treatment of cartilage defects of the knee. [Extracted from the article]

A research paper from the Dr. Sivanthi Aditanar College of Engineering in Tiruchendur, India, explores the analysis of Bipolar Disorder (BD) using structural magnetic resonance imaging (MRI) and a supervised learning framework. The study aims to identify specific abnormalities in brain cells that indicate BD. The researchers employed a 3D texture analysis approach, extracting texture features from the MRI data and using a supervised learning algorithm called Support Vector Machine (SVM) to classify samples as healthy or abnormal. The results showed promising outcomes, with the SVM model achieving an accuracy of 90.42%. This research provides valuable insights into the application of supervised learning and MRI data analysis for understanding BD. [Extracted from the article]

A study conducted by researchers at Henan Normal University in China has proposed a new method for monitoring the training load of athletes using sensor technology and motion image analysis. The researchers combined the advantages of vision-based motion monitoring and wearable sensor-based motion monitoring to create a method that can be used for motion monitoring anytime and anywhere. They also developed a two-stage neural network motion monitoring algorithm to identify different types of motions separately, which improves the accuracy of recognition. Additionally, the study introduced a data enhancement algorithm based on acceleration data to increase the number of samples without the need for re-collecting data. The results of simulation experiments showed that this method can be applied to real-life situations. [Extracted from the article]

A recent study conducted by researchers at the ECE Department in Vijayawada, India, examined the effectiveness of machine learning techniques for the early detection of type 2 diabetes. The researchers analyzed various papers and methodologies, assessing the accuracy of these methods. They proposed a comprehensive framework that utilizes current machine learning algorithms and incorporates novel technologies to enhance the accuracy and reliability of diabetes prediction. The study emphasizes the socio-economic impact of early diabetes detection and highlights the importance of timely interventions in reducing healthcare expenses and improving patient outcomes. The research contributes to global efforts to combat diabetes and improve public health. [Extracted from the article]

A study conducted by researchers at the University of Salerno in Italy has developed a user-friendly algorithm to assist surgeons in pedicle arthrodesis operations for spine surgery. The algorithm uses 3D scans of a patient's vertebra to identify the optimal direction for screw insertion and select the best screws to maximize bone occupation without intersecting each other or going through the walls of the pedicle and vertebral body. The algorithm has been tested on six vertebrae and allows surgeons to freely choose the entry point for the screw. This research represents a novel approach in the field and provides surgeons with a tool for planning their surgeries and creating customized surgical templates. [Extracted from the article]

The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. Keywords: Algorithms; Blood Diseases and Conditions; Bloodstream Infection; Cyborgs; Diagnostics and Screening; Emerging Technologies; Genetics; Health and Medicine; Machine Learning; Sepsis; Septicemia EN Algorithms Blood Diseases and Conditions Bloodstream Infection Cyborgs Diagnostics and Screening Emerging Technologies Genetics Health and Medicine Machine Learning Sepsis Septicemia 399 399 1 10/16/23 20231019 NES 231019 2023 OCT 19 (NewsRx) -- By a News Reporter-Staff News Editor at Blood Weekly -- Data detailed on sepsis have been presented. [Extracted from the article]

A research paper from the Kalasalingam Academy of Research and Education in Tamil Nadu, India, introduces a model called Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The model incorporates the Taylor series with the Tunicate Swarm Algorithm and uses the Yeo-Johnson transformation for data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder, and the fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The research concludes that the developed model is effective and efficient, with high accuracy, a low false positive rate, and a high true positive rate. [Extracted from the article]

A recent study conducted at the Central University of Haryana in India has proposed a privacy-preserving collaborative model for the classification of brain tumors using magnetic resonance imaging (MRI) images. The study utilized deep learning and transfer learning algorithms to analyze an open-source dataset of MRI images, which included four types of tumors and no tumor. The researchers found that federated deep learning models, involving multiple clients, outperformed conventional pretrained models in terms of accuracy. The proposed framework offers a solution for early diagnosis of brain tumors while ensuring data privacy for edge devices with limited resources. [Extracted from the article]

A report from Kerman University of Medical Sciences discusses the use of natural language processing (NLP) to extract cancer concepts from clinical notes automatically. The study systematically reviewed papers that used NLP methods to identify cancer concepts from clinical notes. The researchers found that rule-based algorithms were the most commonly used for concept extraction, and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Unified Medical Language System (UMLS) were the most commonly used terminologies. The study concluded that the use of NLP for extracting cancer concepts has increased in recent years, and suggested that future studies use rule-based algorithms for extracting concepts of other diseases as well. [Extracted from the article]

Bones of mice with osteoporosis that received the treatment were similar to those of the control group, and the treatment showed no evidence of toxicity. Keywords: Algorithms; American Chemical Society; Antiinfectives; Antimalarial Agents; Antiprotozoal Agents; Asia; Bone Research; Chemistry; China; Drugs and Therapies; Genetics; Health and Medicine; Metabolic Bone Diseases and Conditions; Mosquito-Borne Diseases; Musculoskeletal Diseases and Conditions; Osteoporosis EN Algorithms American Chemical Society Antiinfectives Antimalarial Agents Antiprotozoal Agents Asia Bone Research Chemistry China Drugs and Therapies Genetics Health and Medicine Metabolic Bone Diseases and Conditions Mosquito-Borne Diseases Musculoskeletal Diseases and Conditions Osteoporosis 357 357 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Zika & Mosquito Week -- Correction (): The paper's title has been corrected to "Deep Learning-Predicted Dihydroartemisinin Rescues Osteoporosis by Maintaining Mesenchymal Stem Cell Stemness through Activating Histone 3 Lys 9 Acetylation Artificial intelligence has exploded in popularity and is being harnessed by some scientists to predict which molecules could treat illnesses, or to quickly screen existing medicines for new applications. [Extracted from the article]

To this end, this paper proposes an agent-based algorithm for the protection for user privacy health topics based on identity replacement." Keywords: Zhejiang; People's Republic of China; Asia; Engineering; Algorithms; Cybersecurity; Health and Medicine; Public Health EN Zhejiang People's Republic of China Asia Engineering Algorithms Cybersecurity Health and Medicine Public Health 8478 8478 1 10/30/23 20231103 NES 231103 2023 NOV 3 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New research on Engineering is the subject of a report. [Extracted from the article]

Jammu Kashmir, India, Asia, Algorithms, Cancer, Cervical Cancer, Diagnostics and Screening, Health and Medicine, Oncology, Women's Health Keywords: Jammu Kashmir; India; Asia; Algorithms; Cancer; Cervical Cancer; Diagnostics and Screening; Health and Medicine; Oncology; Women's Health EN Jammu Kashmir India Asia Algorithms Cancer Cervical Cancer Diagnostics and Screening Health and Medicine Oncology Women's Health 1014 1014 1 10/03/23 20231006 NES 231006 2023 OCT 3 (NewsRx) -- By a News Reporter-Staff News Editor at Women's Health Weekly -- A new study on Oncology - Cervical Cancer is now available. [Extracted from the article]

The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. "In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP), which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. [Extracted from the article]

To address this challenge, the paper introduces an accurate binary prostate cancer classification algorithm that integrates linear support vector machines (SVM) and ridge regression-based four-feature selection algorithms. Keywords: Algorithms; Cancer; Diagnostics and Screening; Health and Medicine; Machine Learning; Oncology; Prostate Cancer; Prostatic Neoplasms; Support Vector Machines EN Algorithms Cancer Diagnostics and Screening Health and Medicine Machine Learning Oncology Prostate Cancer Prostatic Neoplasms Support Vector Machines 1492 1492 1 09/11/23 20230912 NES 230912 2023 SEP 12 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Investigators publish new report on prostate cancer. [Extracted from the article]

Keywords: Algorithms; Computers; Endoscopy; Health and Medicine; Minimally Invasive Surgical Procedures; Surgery EN Algorithms Computers Endoscopy Health and Medicine Minimally Invasive Surgical Procedures Surgery 686 686 1 08/28/23 20230903 NES 230903 2023 SEP 3 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Devices & Surgical Technology Week -- Research findings on endoscopy are discussed in a new report. Algorithms, Computers, Endoscopy, Health and Medicine, Minimally Invasive Surgical Procedures, Surgery. [Extracted from the article]

Researchers from the University of Electronic Science and Technology of China have developed an intelligent inspection guidance system for urethral endoscopy. The system combines image processing, simultaneous localization and mapping (SLAM), and intelligent navigation technologies to assist doctors in conducting a quick and comprehensive examination. The system uses a generative adversarial network-based deblurring algorithm and a vascular attention-based feature extraction algorithm to improve accuracy in identifying urethral vascular features. The research concludes that the proposed method is more accurate and comprehensive, resulting in a 4.34% accuracy improvement. [Extracted from the article]

A recent study conducted by researchers at the University of California San Diego has shown that AI algorithms, combined with a modified pacifier, can determine if newborns are nursing properly. The study measured the strength and regularity of babies' suckling patterns using eight independent parameters. The results provide objective data that can improve standard assessments and potentially prevent surgical interventions. The study also found that the device-generated data could improve the subjective evaluation of infants' suckling ability by clinicians. The researchers hope that their findings will support parents struggling with breastfeeding and improve long-term health outcomes. [Extracted from the article]

A report from Yangtze University in Jingzhou, China discusses the use of deep learning algorithms in food safety and authenticity detection. The researchers highlight the advantages of deep learning algorithms over traditional machine learning algorithms and chemometric algorithms. They specifically focus on convolutional neural networks (CNN), fully convolutional networks (FCN), and generative adversarial networks (GAN). The report also explores the potential for further development in this field, including the use of data augmentation and multimodal neural networks. The research concludes that deep learning algorithms have made significant progress in food safety and authenticity detection, but there is still room for improvement. [Extracted from the article]

A recent study conducted by researchers from the Icahn School of Medicine at Mount Sinai in New York City explores the use of advanced deep learning algorithms for magnetic resonance imaging (MRI) segmentation. Accurate MRI segmentation is crucial for diagnosing abnormalities and planning treatments, but traditional supervised learning techniques require a large amount of annotated data. The study reviews state-of-the-art algorithms that utilize a limited number of annotated samples, including self-supervised learning, generative models, few-shot learning, and semi-supervised learning. The researchers also discuss future directions in the field, such as contrastive language-image pretraining, to further improve image segmentation with limited labels. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

Researchers from Brigham and Women's Hospital in Boston, Massachusetts have published a report on algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation (TMS). TMS is a device-based neuromodulation technique used to treat brain diseases. The report discusses modern algorithms for real-time TMS electric field modeling, including basis representation techniques and deep neural-network-based methods. It also reviews software tools that integrate electric field modeling with navigated TMS. The research was supported by the National Institutes of Health (NIH) in the United States. [Extracted from the article]

This article discusses the use of machine learning algorithms, specifically the K-Nearest Neighbors (KNN) algorithm, in predicting vaccine targets. Traditional methods for identifying suitable antigen targets for vaccines can be time-consuming and lack scalability. The authors present an implementation of the KNN algorithm for vaccine target prediction and demonstrate its effectiveness in accurately identifying vaccine targets. The study highlights the potential of the KNN algorithm in this field and provides insights for future research. Please note that this article has not been peer-reviewed. [Extracted from the article]

A study conducted at Velagapudi Ramakrishna Siddhartha Engineering College explores the use of a genetic algorithm-based decision tree to detect dementia through MRI analysis. The researchers emphasize the importance of early and accurate detection of dementia for optimal therapy and management. By analyzing a large collection of MRI images and associated clinical data, the decision tree algorithm achieved an impressive accuracy rate of 96.67%. This research contributes to the development of a non-invasive diagnostic tool for dementia detection. [Extracted from the article]

A new study conducted by researchers at Quantum University focuses on hydrocephalus, a central nervous system disorder that primarily affects infants and toddlers. The study aims to improve the accuracy of hydrocephalus diagnosis by developing an algorithm called H-detect. This algorithm utilizes image processing techniques and the CatBoost algorithm to predict hydrocephalus more effectively than traditional methods. The research concludes that the H-detect strategy outperforms conventional approaches. For more information, readers can refer to the journal article "H-Detect: an Algorithm for Early Detection of Hydrocephalus" published in Informatika i avtomatizacia. [Extracted from the article]

A study conducted by researchers at Prince Sattam bin Abdulaziz University in Saudi Arabia explores the issue of balancing generation and demand in interconnected microgrids (MGs) to address security, power quality, and reliability concerns. The study proposes an optimized fractional order load frequency control (LFC) scheme with cascaded outer and inner control loops. The proposed controller, based on a modified version of the liver cancer optimization algorithm (MLCA), achieves better disturbance rejection compared to traditional LFC methods. The study also considers the contribution of electric vehicles and the high penetration of renewables to test the stability of the proposed LFC technique. The results confirm the superior response and improved performance of the proposed controller. [Extracted from the article]

A study conducted by researchers at Guangxi University for Nationalities in Nanning, China, proposes a new method for diagnosing Parkinson's disease based on speech signals. The researchers developed a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm (BFAHA) to selectively extract disease-specific information from speech signals, improving accuracy and reducing the number of features used. The algorithm was tested on high-dimensional datasets and showed competitive performance compared to other algorithms. This research has been peer-reviewed and published in the Journal of Bionic Engineering. [Extracted from the article]

A recent study conducted at Shiraz University aimed to improve the accuracy of breast cancer diagnosis by using a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods. The researchers tested their algorithm on three common breast cancer datasets and found that it significantly improved accuracy while reducing the number of features needed for diagnosis. The study suggests that the proposed algorithm has the potential to enhance breast cancer diagnosis and reduce cost and time. Further information on the study can be found in the International Journal of Computational Intelligence Systems. [Extracted from the article]

A study conducted by researchers at Zhengzhou University of Aeronautics explores the optimization of overseas surgical warehouse locations in cross-border medical supply chain networks using parallel big data mining. The researchers propose an algorithm that takes into account factors such as cross-border transportation cost, local transportation cost, warehouse operation cost, and market value to determine the best location for overseas warehouses. By introducing the concept of time window and customer satisfaction, a multi-objective location model is established. The study concludes that the proposed algorithm effectively improves user satisfaction, optimal probability, and reduces system costs. [Extracted from the article]

Researchers at Chitkara University in Punjab, India have developed an improved lung cancer segmentation technique using nature-inspired optimization approaches. The study focused on the precise identification of tumor nodules in lung cancer Computed Tomography (CT) images. The researchers integrated four nature-inspired swarm intelligent techniques with the K-means algorithm to address the visual challenges of segmentation. The results showed that the K-means algorithm with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation compared to other optimization approaches. This research provides a valuable tool for researchers to improve the segmentation of medical image datasets for identifying the targeted region of interest. [Extracted from the article]

A research team at the University of Chicago Medical Center has developed a new risk score, called the U.S. Candidate Risk Score (US-CRS), to predict the likelihood that a patient will die without a heart transplant. The US-CRS aims to address the limitations of the current therapy-based 6-status system by incorporating objective physiological measurements, such as levels of molecules in the blood associated with organ failure. The researchers analyzed data from over 16,900 adult heart transplant candidates to determine the relationship between the US-CRS and mortality within 6 weeks of being placed on the transplant waitlist. The US-CRS model was found to be much better at predicting mortality than the current U.S. heart allocation system. However, further validation and committee approval are needed before the US-CRS can be adopted. The researchers are also working on improving heart allocation overall and designing a fair and resilient continuous distribution system for deceased donor hearts. [Extracted from the article]

A recent study conducted by researchers at the First Affiliated Hospital of Wenzhou Medical University in China explores the use of deep learning algorithms in medical image analysis. The study highlights the importance of employing advanced deep learning techniques in this field, as they have shown impressive results in various areas of healthcare. The researchers categorized and assessed different deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), based on their principles, advantages, limitations, methodologies, and datasets. The study also addresses the challenges that hinder the widespread implementation of deep learning in medical image analysis. The findings of this research provide valuable insights for future studies and advancements in image analysis in medical healthcare research. [Extracted from the article]

Keywords: Algorithms; Cyborgs; Emerging Technologies; Epidemiology; Health and Medicine; Hemic and Immune Systems; Immunology; Lymph Nodes; Lymphoid Tissue; Machine Learning; Neoplasms; Risk and Prevention EN Algorithms Cyborgs Emerging Technologies Epidemiology Health and Medicine Hemic and Immune Systems Immunology Lymph Nodes Lymphoid Tissue Machine Learning Neoplasms Risk and Prevention 4761 4761 1 11/06/23 20231110 NES 231110 2023 NOV 10 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- A new study on neoplasms is now available. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform.". [Extracted from the article]

Keywords: Guimaraes; Portugal; Europe; Algorithms; Basal Ganglia Diseases and Conditions; Brain Diseases and Conditions; Central Nervous System Diseases and Conditions; Diagnostics and Screening; Health and Medicine; Movement Disorders; Nervous System Diseases and Conditions; Neurodegenerative Diseases and Conditions; Parkinson's Disease; Parkinsonian Disorders; Technology EN Guimaraes Portugal Europe Algorithms Basal Ganglia Diseases and Conditions Brain Diseases and Conditions Central Nervous System Diseases and Conditions Diagnostics and Screening Health and Medicine Movement Disorders Nervous System Diseases and Conditions Neurodegenerative Diseases and Conditions Parkinson's Disease Parkinsonian Disorders Technology 1245 1245 1 11/06/23 20231106 NES 231106 2023 NOV 6 (NewsRx) -- By a News Reporter-Staff News Editor at Pain & Central Nervous System Week -- A new study on Neurodegenerative Diseases and Conditions - Parkinson's Disease is now available. Keywords for this news article include: Guimaraes, Portugal, Europe, Algorithms, Basal Ganglia Diseases and Conditions, Brain Diseases and Conditions, Central Nervous System Diseases and Conditions, Diagnostics and Screening, Health and Medicine, Movement Disorders, Nervous System Diseases and Conditions, Neurodegenerative Diseases and Conditions, Parkinson's Disease, Parkinsonian Disorders, Technology, University of Minho. Guimaraes, Portugal, Europe, Algorithms, Basal Ganglia Diseases and Conditions, Brain Diseases and Conditions, Central Nervous System Diseases and Conditions, Diagnostics and Screening, Health and Medicine, Movement Disorders, Nervous System Diseases and Conditions, Neurodegenerative Diseases and Conditions, Parkinson's Disease, Parkinsonian Disorders, Technology. [Extracted from the article]

According to the news editors, the research concluded: "The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image." To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm.". [Extracted from the article]

This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. Keywords: Algorithms; Cyborgs; Diabetic Angiopathies; Diabetic Retinopathy; Emerging Technologies; Eye Diseases and Conditions; Health and Medicine; Machine Learning; Networks; Neural Networks; Nutritional and Metabolic Diseases and Conditions; Ophthalmology; Retinal Diseases and Conditions; Retinopathy; Vascular Diseases and Conditions EN Algorithms Cyborgs Diabetic Angiopathies Diabetic Retinopathy Emerging Technologies Eye Diseases and Conditions Health and Medicine Machine Learning Networks Neural Networks Nutritional and Metabolic Diseases and Conditions Ophthalmology Retinal Diseases and Conditions Retinopathy Vascular Diseases and Conditions 107 107 1 10/30/23 20231030 NES 231030 2023 OCT 30 (NewsRx) -- By a News Reporter-Staff News Editor at Diabetes Week -- New study results on diabetic retinopathy have been published. [Extracted from the article]

Keywords: Msila; Algeria; Algorithms; Cancer; Genetics; Health and Medicine; Oncology EN Msila Algeria Algorithms Cancer Genetics Health and Medicine Oncology 684 684 1 10/30/23 20231031 NES 231031 2023 OCT 31 (NewsRx) -- By a News Reporter-Staff News Editor at Cancer Weekly -- Current study results on Cancer have been published. This paper proposes a new bio-inspired algorithm for cancer classification in gene selection called Binary Grey Wolf Optimization Algorithm (BGWOA), which is based on hybridization between Minimum Redundancy-Maximum Relevance (MRMR) and a novel Binary Grey Wolf algorithm. [Extracted from the article]

Keywords: Algorithms; Gliomas; Health and Medicine; Oncology EN Algorithms Gliomas Health and Medicine Oncology 249 249 1 10/09/23 20231010 NES 231010 2023 OCT 9 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Oncology Week -- Research findings on gliomas are discussed in a new report. According to the news editors, the research concluded: "This indicates that the algorithm proposed in this paper is an effective automatic glioma segmentation algorithm.". [Extracted from the article]

Keywords: Algorithms; Cardiology; Cardiovascular; Cardiovascular Research; Engineering; Health and Medicine EN Algorithms Cardiology Cardiovascular Cardiovascular Research Engineering Health and Medicine 497 497 1 10/03/23 20231006 NES 231006 2023 OCT 2 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Data detailed on engineering have been presented. Algorithms, Cardiology, Cardiovascular, Cardiovascular Research, Engineering, Health and Medicine This paper presents a computerized method with an intelligent algorithm for detecting various adventitious sounds that are the key characteristics of cardiopulmonary diseases (CD) and assists the doctor/physician in the continuous diagnosis of lungs, which potentially can be beneficial during the COVID-19 progression. [Extracted from the article]