Search

A report from the North University of China discusses the detection of abnormal blood flow regions using near-infrared correlation spectroscopy. The researchers used the NL-Bregman-TV imaging algorithm to visualize blood flow, but found that the resolution was too low for practical use. To improve the resolution, they employed the bicubic interpolation method and proposed a parameter index to evaluate its effectiveness. They also developed a threshold segmentation algorithm and a morphological processing algorithm to accurately locate and extract the contour of abnormal blood flow. This research has important implications for the diagnosis and treatment of various diseases. [Extracted from the article]

The article focuses on a review from the University of Applied Sciences and Arts Western Switzerland about machine learning techniques for diagnosing Alzheimer's disease. Topics include the application of machine learning for early diagnosis of Alzheimer's and mild cognitive impairment, the use of magnetic resonance imaging in these techniques, and a comparative analysis of various algorithms' effectiveness in classification performance.

A team of engineers at the California Institute of Technology (Caltech) has developed a new microscopy technique called APIC (Angular Ptychographic Imaging with Closed-form method) that can obtain clear, high-resolution images without blurriness or distortion. This technique outperforms the previous method, FPM (Fourier ptychographic microscopy), by taking fewer measurements and eliminating the need for trial and error. APIC solves a linear equation to correct for aberrations introduced by a microscope's optical system, resulting in clear images with a large field of view. This advancement could have applications in biomedical imaging, digital pathology, and drug screening. [Extracted from the article]

Researchers from National Chung Hsing University in Taiwan have published a study on the detection of white matter hyperintensities (WMHs) in brain magnetic resonance images. WMHs are lesions associated with Alzheimer's disease and cognitive decline. The researchers developed a new algorithm called iterative KCEM (IKCEM) to improve the detection of WMHs. The algorithm was evaluated using two commonly used software packages and showed superiority over existing methods. The study provides valuable insights into the diagnosis of Alzheimer's disease and could contribute to advancements in medical imaging technology. [Extracted from the article]

A research report from Soochow University in Suzhou, China, introduces a new algorithm called Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MASDSC) for brain tumor clustering in medical imaging. The algorithm addresses the limitations of traditional unsupervised deep subspace clustering algorithms by incorporating a semi-supervised learning framework and a multi-scale feature extraction mechanism. It also employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters. The MAS-DSC algorithm achieves excellent clustering results on standard datasets and demonstrates robustness and enhanced clustering performance on a brain tumor dataset. The research has been peer-reviewed and published in Computers Materials & Continua. [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]

Researchers from Linkoping University in Sweden have developed an enhanced chameleon swarm algorithm (ICSA) for global optimization and multi-level thresholding medical image segmentation. Medical image segmentation is crucial for disease diagnosis and analysis, but selecting optimal thresholds can be computationally intensive. The ICSA algorithm improves precision and efficiency by introducing the concept of the "best random mutation strategy" and utilizing three distribution functions for mutating search individuals. Comparative analysis shows that ICSA outperforms other metaheuristic algorithms and significantly improves image segmentation performance. This research has been peer-reviewed and published in Neural Computing and Applications. [Extracted from the article]

A researcher from the University of Science and Technology Beijing has conducted a study on the predictive analysis of brain imaging data using deep learning algorithms. The study aimed to improve the accuracy and efficiency of brain imaging data prediction. The researcher developed a batch normalized convolutional neural network (BN-CNN) based prediction method, which showed promising results in predicting missing structural data in brain imaging. The algorithm was applied to epilepsy and Parkinson's cases, achieving an average prediction accuracy of 93.37%. The study suggests that deep learning algorithms have the potential to greatly benefit clinical diagnosis and treatment in the future. [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]

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 report from the National Institute of Technology Silchar in Assam, India introduces a robust zero-watermarking technique for medical images. The technique utilizes the Collatz and Fresnelet Transforms to calculate invisible data in medical imagery, ensuring data integrity and authenticity without compromising diagnostic accuracy. The approach incorporates a dual-layer security strategy and outperforms existing methods in medical image watermarking, demonstrating resilience against various manipulations. The research concludes that this approach can significantly improve data security and reliability in medical imaging applications. [Extracted from the article]

A report from the Universidad Autonoma de Queretaro discusses research on multiple sclerosis (MS) and the use of magnetic resonance imaging (MRI) for diagnosis and treatment analysis. The report proposes an MRI preprocessing algorithm that performs morphological transformations to filter out undesirable components and extract relevant structures, such as MS lesions. The algorithm also computes granulometry measurements to describe the size qualities of the lesions. Two artificial neural networks (ANNs) were trained using this algorithm to predict MS diagnoses, and the results showed high accuracy in supporting specialists in making diagnostic decisions and estimating disease progress. [Extracted from the article]

The article presents new research from Shenyang University on a gastrointestinal image classification method using an improved Adam algorithm, AdamW_AGC, which enhances classification accuracy and training stability. Topics include the challenges in medical image classification, the proposed AdamW_AGC algorithm, and experimental results demonstrating its effectiveness on various datasets.

The article focuses on research from Sikkim Manipal University regarding a novel machine learning methodology for imaging intervertebral disc degeneration. Topics include the integration of microscopic histopathological features with macroscopic MRI signal intensities, the development of a hybrid imaging tool for improved assessment, and the use of machine learning algorithms to predict degenerative changes.

A study conducted in Beijing, China, focused on brain cancer, specifically gliomas, which have the highest mortality rate among brain tumors. The researchers proposed a hierarchical multi-scale attention feature fusion medical image classification network (HMAC-Net) to improve the classification of glioma risk. The network framework consists of three parallel layers: global feature extraction, local feature extraction, and multi-scale feature fusion. The results of the study showed that HMAC-Net performed the best in both qualitative and quantitative analysis. The research has been peer-reviewed and published in Computers Materials & Continua. [Extracted from the article]

A recent study conducted at Mohamed Sathak Engineering College in Tamil Nadu, India, focused on the detection and classification of brain tumors using magnetic resonance imaging (MRI). The researchers proposed an algorithm called Adaptive Convolutional Autoencoder-based Snow Avalanches (ACAE-SA) to automate the detection and categorization of brain tumors from MRI images. The algorithm was found to be effective and outperformed existing techniques. This research has been peer-reviewed and published in the journal Signal Image and Video Processing. For more information, readers can contact the researchers at Mohamed Sathak Engineering College. [Extracted from the article]

A new imaging method for biological samples has been developed by IOCB Prague and the University of Munster. This method uses software to quickly and accurately identify chemicals in tissues, allowing researchers to visualize their distribution in organs. The software, called SIMSEF, was developed in collaboration with medical experts and can accelerate the detection of clinical biomarkers for diagnostic purposes. It is part of the open-source software package MZmine, which has been used by experts worldwide since 2005. The new algorithm enhances the capabilities of MZmine and allows for the linking of different types of data, including analytical and imaging methods. [Extracted from the article]

Researchers from Deenbandhu Chhotu Ram University of Science and Technology in India have published a new study on information technology management. The study focuses on the development of a brain-computer interface using a genetic algorithm with modified genome and phenotype structures. The researchers propose a novel scheme that combines genetic algorithms, support vector machines, and wavelet packet transform for motor imagery classification and optimal channel selection. The scheme outperforms existing methods in terms of classification accuracy and number of channels. This research contributes to the advancement of human-machine interface technology. [Extracted from the article]

A new study conducted by researchers at Hunan University in China proposes a novel method for enhancing the performance of glaucoma classification. The researchers integrated the cuckoo search algorithm with Caputo fractional order to improve feature selection. They conducted experiments using various feature extraction techniques and evaluated the informative features selected from their proposed method. The results showed superior performance, achieving high accuracy, precision, F1-Score, specificity, sensitivity, and Matthew's correlation coefficient. The researchers concluded that their method is efficient and applicable in ophthalmology. For more information, readers can refer to the journal article published in Diagnostics. [Extracted from the article]

A patent application has been submitted for a device and method to assist with the detection and classification of bone fractures based on medical images. The device uses algorithms and neural networks to identify and localize fractures within the bone structure. It can also suggest possible treatment options based on the classification of the fracture. The device can be used manually with user input or automatically with algorithm support. The invention aims to provide assistance to surgeons in detecting and classifying bone fractures, ultimately improving patient care. [Extracted from the article]

Researchers from Catholic University Louvain (UCLouvain) in Belgium have developed a new algorithm for analyzing diffusion-weighted magnetic resonance imaging (MRI) data. The algorithm, called blind spherical deconvolution, allows for the estimation of white matter fiber microstructure without requiring exact knowledge of the response function. While the algorithm performed well in synthetic data simulations, it was outperformed by other state-of-the-art methods when tested on real in-vivo data. The researchers believe that the algorithm has potential for deriving per-voxel per-direction metrics in both synthetic and in-vivo data. [Extracted from the article]

A recent report from investigators at Satbayev University in Alma Ata, Kazakhstan explores the use of artificial intelligence (AI) methods and machine learning algorithms for diagnosing Alzheimer's disease (AD). The review discusses the importance of accurate AD diagnosis and the potential benefits of using AI techniques. The researchers examine various data sources, including MRI data, PET imaging, EEG and MEG signals, and data from sensors, to extract information that may not be visible to the human eye. The review also discusses the use of handwriting data and AI algorithms for early detection of cognitive loss associated with AD. The research concludes with a summary of significant findings and their clinical implications. [Extracted from the article]

A researcher from the Pan African University Institute for Basic Sciences has discussed their research on improving magnetic resonance imaging (MRI) using compressed sensing and adaptive multi-extreme particle swarm optimization. MRI is a powerful technique for examining the body's internal structure, but its lengthy acquisition times can limit its clinical usefulness. The researcher proposes a novel method that combines adaptive multi-extreme particle swarm optimization and dual tree complex wavelet transform to speed up image acquisition in MRI. The results show improvements in image quality and suggest that this method could potentially enable high-quality, real-time MRI imaging, leading to better detection and treatment of medical conditions. [Extracted from the article]

A recent study conducted by researchers from Yanshan University in China explores the issue of inaccuracy in inhomogeneous sound speed fields in photoacoustic imaging (PAI), which can result in blurred and distorted images. The researchers propose a data-driven speed field inversion method that combines clustering with updates to the speed field. By dividing the model according to tissue similarity and adjusting the step length of sound speeds, the method improves the accuracy of the sound speed field and enhances the imaging performance of PAI. The study provides valuable insights into improving the quality of PAI without the need for additional information or equipment. [Extracted from the article]

A recent study conducted by researchers at Tianjin University in China has developed a blind watermarking scheme for copyright protection of medical and non-medical images. The scheme utilizes the QZ algorithm to embed the watermark into the host image, rather than using an addition-based process. The researchers found that their scheme was robust against various image-processing attacks and forgery attacks, while maintaining good invisibility. The study concluded that the proposed scheme outperformed existing addition-based schemes in terms of robustness. This research has been peer-reviewed and published in Expert Systems with Applications. [Extracted from the article]

A recent study conducted by researchers at Manipal Academy of Higher Education in India has developed a new approach to enhance precision in medical imaging. The study focuses on the detection of fiducial markers in neuroimaging, which are crucial reference points for accurate spatial alignment during image registration. The researchers propose a model that addresses challenges in marker placement and variability during MRI scanning, ensuring reliable localization for subsequent analysis. The model utilizes a 3D Convolutional Neural Network (CNN) and achieved exceptional accuracy and sensitivity in detecting fiducial points. The study concludes that this algorithm has the potential to improve accuracy and efficiency in neuroimaging studies. [Extracted from the article]

A report from Zagazig University in Egypt discusses the need for high levels of security in the transmission of medical data in Internet of Medical Things (IoMT) systems. The researchers propose a new algorithm that utilizes a binary Coronavirus disease optimization algorithm to generate optimal secret keys for chaotic map encryption of medical images. The algorithm is found to be robust against various attacks and outperforms similar encryption techniques. The research has been peer-reviewed and published in Neural Computing and Applications. [Extracted from the article]

Keywords: Algorithms; Fuzzy Logic; Health and Medicine; Healthcare Engineering EN Algorithms Fuzzy Logic Health and Medicine Healthcare Engineering 2023 MAR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Fresh data on healthcare engineering are presented in a new report. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. [Extracted from the article]

Keywords: Algorithms; Cyborgs; DOE/Los Alamos National Laboratory; Emerging Technologies; Machine Learning EN Algorithms Cyborgs DOE/Los Alamos National Laboratory Emerging Technologies Machine Learning 2023 FEB 25 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- A team of scientists at Los Alamos National Laboratory is applying machine-learning algorithms to subsurface imaging that will impact a variety of applications, including energy exploration, carbon capture and sequestration and estimating pathways of subsurface contaminant transport, according to new research published in IEEE Signal Processing Magazine. Keywords for this news article include: Cyborgs, Algorithms, Machine Learning, Emerging Technologies, DOE/Los Alamos National Laboratory. [Extracted from the article]

With the advancement of SIM, researchers have proposed a range of algorithms such as Open-SIM, fairSIM, Hessian-SIM, HiFi-SIM, etc. Keywords: Algorithms; Peking University; Software; Technology EN Algorithms Peking University Software Technology 159 159 1 08/07/23 20230812 NES 230812 2023 AUG 12 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Structured illumination microscopy (SIM) has emerged as the most powerful super-resolution technique for live cell imaging, owing to its inherent advantages of high speed, low phototoxicity, and compatibility with various dyes. [Extracted from the article]

A preprint abstract discusses the challenges of discovering causal relationships from observational neuroimaging data, such as fMRI. The article highlights the limitations of popular algorithms and proposes a new algorithm called CaLLTiF, which improves upon existing methods in terms of computational efficiency and statistical error control. The algorithm is tested on simulated fMRI data and shows higher accuracy in recovering ground-truth connectivity. When applied to resting-state human fMRI, CaLLTiF reveals consistent causal connectomes across individuals, demonstrates top-down flow of causal effects, and exhibits statistically significant laterality and gender differences. The research aims to improve the accuracy and scalability of causal discovery from whole-brain fMRI data. [Extracted from the article]

A patent has been issued to metamorphosis GmbH for a device and method to assist with bone fracture detection and classification. The invention aims to provide surgeons with assistance in detecting and classifying bone fractures based on medical images. The device uses algorithms to identify bone structures, determine fracture lines, and detect bone features. It can also classify bone fractures and propose treatment options. The patent provides detailed information on the device's functionality and potential applications. [Extracted from the article]

A recent report from Capital Medical University in Beijing, China discusses the use of neuroimaging technology in diagnosing brain diseases. The researchers developed a deep neural network imaging technology based on the deep learning algorithms of neural imaging techniques. They used this technology to solve binary classification problems related to Alzheimer's disease and Mild Cognitive Impairment. The results showed that the deep neural network with stack auto-encoder feature extraction performed better than other methods, with an accuracy rate of 74.9%. This research provides valuable insights into the potential of deep learning algorithms in diagnosing brain diseases. [Extracted from the article]

A research report from the University of Science and Technology of China discusses the use of dual-frequency ultrasounds in tumor treatment. The researchers propose a novel dual-frequency impedance matching network design using a genetic algorithm to compute component values. The implementation of this design resulted in improved transmission efficiency and expanded ablation zones in thermal ablation experiments. The researchers conclude that this dual-frequency impedance matching method holds promising prospects for application in dual-frequency ultrasound treatment. [Extracted from the article]

A recent report from the Alma Mater Studiorum - University of Bologna discusses the need for a simplified and cost-effective diagnostic algorithm for the surveillance of intraductal papillary mucinous neoplasms (IPMNs). The increased detection of pancreatic cysts has led to extensive diagnostic investigations, resulting in many patients undergoing numerous imaging follow-up studies for years. The researchers propose the use of unenhanced abbreviated-MRI (A-MRI) protocols as a surveillance tool for patients with IPMN, as it has shown high diagnostic performance in malignant detection. The aim of the study is to determine if implementing an A-MRI protocol could improve healthcare economics and reduce waiting lists without significantly reducing diagnostic accuracy. [Extracted from the article]

A report from Prince of Songkla University in Phuket, Thailand discusses the use of steganography, a technique for hiding sensitive information within a cover image, to enhance the security of medical information in chest X-ray images. The researchers developed four improved algorithms that allow for the hiding of full-size images within the cover image, addressing limitations of previous methods. The results of the experiment showed advancements in image quality and payload capacity compared to previous studies. This research has potential applications in enhancing the security of patient information in medical settings. [Extracted from the article]

A recent report from Oslo University Hospital discusses the development of a new algorithm called FEMA (Fast and Efficient Mixed-effects Algorithm) for analyzing large-scale neuroimaging datasets. The algorithm, which addresses the heavy computational requirements of the linear mixed-effects model (LME), allows for advanced investigation of the relationships between neuroimaging metrics and variables of interest. The researchers validated FEMA through extensive simulations and applied it to real data to study the effects of age on cortical thickness and functional connectivity in the brain. The source code for FEMA is available online, and the research was funded by various organizations including the NIH and the European Union's Horizon 2020 program. [Extracted from the article]

A recent study from Liaoning University explores the application of machine learning in medicine. The researchers highlight the increasing quantity and complexity of medical data, such as medical images and genomics data, and the limitations of traditional analysis methods. Machine learning algorithms offer new opportunities for medical research and clinical practice by learning patterns and prediction models from large-scale data. The study examines the use of machine learning in medical image diagnosis, genomics, and drug discovery, emphasizing its potential to process complex medical data and support personalized medical care. [Extracted from the article]

A new report from Guilin University of Electronic Technology in China discusses the use of electrical impedance tomography (EIT) as a non-invasive imaging method. EIT allows for the acquisition of resistivity distribution information within an object without the use of radiation. The researchers propose a new algorithm based on a graph convolutional neural network model to improve the image reconstruction process in EIT. The experimental results demonstrate the effectiveness of the proposed algorithm across different domain shapes, grids, and non-distributed data. [Extracted from the article]

Researchers from Shahid Beheshti University of Medical Sciences in Tehran, Iran have developed a deep learning algorithm to enhance the quality of Magnetic Resonance Imaging (MRI) images and generate synthetic Computed Tomography (CT) images for more accurate radiotherapy planning. The study utilized the pix2pix Generative Adversarial Network to synthesize CT images from noisy MRI images of 20 patients with brain disease. The statistical measurements of the synthetic CT images were compared to referenced CT images, and the results showed that the proposed method provides an acceptable level of accuracy for radiotherapy treatment planning. [Extracted from the article]

A study conducted by Soonchunhyang University explores the use of an ensemble convolutional neural network (CNN) model with the line segment feature analysis (LFA) algorithm to classify the stage of Alzheimer's disease (AD) based on the shape of a patient's brain. The researchers combined a convolutional layer from the visual geometry group network (VGGNet) model with a 1D CNN model to improve the accuracy of AD classification. The proposed model demonstrated superior performance compared to existing methods, achieving an accuracy of 0.986 and a loss of 0.0385. This research highlights the importance of early diagnosis for the treatment and management of AD. [Extracted from the article]

A new report from researchers at the Department of Management in Dehradun, India, discusses the use of computer-aided detection (CAD) in classifying brain diseases, with a focus on Alzheimer's Disease (AD) and brain tumors. The study highlights the importance of expertise in interpreting MRI, PET, and CT scans for accurate diagnosis. The researchers propose pre-processing methods that improve the classification performance of MRI images and reduce training time for learning algorithms. The study concludes that their proposed algorithms outperformed existing methods in terms of accuracy and sensitivity. For more information, the full article can be accessed through the provided link. [Extracted from the article]

Algorithms, Diagnostics and Screening, Electroencephalography, Health and Medicine, Neuroscience, Human Neuroscience Keywords: Algorithms; Diagnostics and Screening; Electroencephalography; Health and Medicine; Human Neuroscience; Neuroscience EN Algorithms Diagnostics and Screening Electroencephalography Health and Medicine Human Neuroscience Neuroscience 428 428 1 05/22/23 20230526 NES 230526 2023 MAY 27 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- New study results on human neuroscience have been published. [Extracted from the article]

A preprint abstract from biorxiv.org discusses the development of a fast and efficient mixed-effects algorithm (FEMA) for analyzing large-scale neuroimaging datasets. The algorithm allows for whole-brain vertex-wise, voxel-wise, and connectome-wide linear mixed-effects model (LME) analyses in large samples. The researchers validate FEMA through extensive simulations and demonstrate its applicability by studying the effects of age on cortical thickness and functional connectivity using longitudinal imaging data. The findings reveal distinct spatial patterns in brain maturation during early adolescence. The FEMA algorithm enables advanced investigation of relationships between neuroimaging metrics and variables of interest in a fast and efficient manner. The source code for FEMA is available for access. [Extracted from the article]

Researchers from Ahvaz Jundishapur University of Medical Sciences have conducted a systematic review and meta-analysis to evaluate the use of machine learning (ML) algorithms in diagnosing intracranial hemorrhage (ICH) on computed tomography (CT) scans. The study found that ML algorithms have acceptable performance in diagnosing ICH, with a pooled sensitivity of 0.917 and a pooled specificity of 0.945. The ResNet algorithm showed higher specificity compared to other algorithms. The researchers suggest that using ResNet in ICH detection holds promise for improved prediction through training in an Architecture Learning Network (ALN). [Extracted from the article]

Researchers from National Taiwan University have developed an algorithm called YOLOv8 that can detect fractures in pediatric wrist trauma X-ray images. The algorithm uses data augmentation to improve its performance and has achieved a state-of-the-art mean average precision (mAP 50) of 0.638, which is higher than previous versions of the algorithm. The researchers have also designed an application called "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures and reducing the probability of error analysis. This research provides a valuable tool for surgeons in the field of pediatric wrist trauma. [Extracted from the article]

A recent study conducted by researchers at Sejong University in Seoul, South Korea, explored the use of machine learning algorithms and spatial criteria to model and map driver drowsiness. The study found that four machine learning algorithms, including bagged decision trees and random forest, had high accuracy in estimating drowsiness levels based on electroencephalogram (EEG) signals and spatial factors. The researchers concluded that mapping driver drowsiness using machine learning and spatial criteria can help identify high-risk areas and improve road safety. The study was published in Transportation Research Part F Traffic Psychology and Behaviour. [Extracted from the article]

Keywords: Patna; India; Asia; Mathematics; Algorithms; Genetic Algorithms; Genetics EN Patna India Asia Mathematics Algorithms Genetic Algorithms Genetics 501 501 1 10/16/23 20231020 NES 231020 2023 OCT 21 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- A new study on Mathematics is now available. Keywords for this news article include: Patna, India, Asia, Mathematics, Algorithms, Genetic Algorithms, Genetics, National Institute of Technology Patna. [Extracted from the article]

Keywords: Algorithms; Technology EN Algorithms Technology 5444 5444 1 10/09/23 20231013 NES 231013 2023 OCT 13 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Researchers detail new data in technology. Our news reporters obtained a quote from the research from University of Sargodha: "Kidney stones are a regular but life-threatening disease as they mostly remain unrecognized at the initial stages, leading to an increased threat of end-stage kidney failure. [Extracted from the article]