Disease Diagnosis - Science topic

Early and accurate plant diseases detection is very important for increased agricultural production and guaranteed food safety. In addition to that, traditional methods of disease diagnosis (typical to large scale farming operations), such as manual inspection, are not very efficient, usually labour intensive and less accurate. A systematic review...

This article explores the transformative potential of synthetic data in addressing the challenges of limited data availability in healthcare AI development. It examines various techniques for generating synthetic data, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and the Synthetic Minority Over-sampling Techniq...

Detection of pulmonary diseases from X-ray images is one of the most significant challenges in medicine and deep learning. Traditional image analysis techniques are not efficient enough due to their reliance on manual features and functional limitations. Recent years have seen tremendous advancements in this area in deep neural networks (DNNs) and...

The world is currently facing numerous food security challenges. Plant diseases are among the primary issues, recognized as major contributors to crop losses. These losses significantly undermine food production and the global economy. Traditional plant disease detection methods remain reliant on manual assessments by agricultural experts, which ca...

Kalp hastalıkları, dünya çapında önde gelen ölüm nedenlerinden biri olup, erken teşhis ve doğru tedavi planlaması hastaların yaşam kalitesi ve hayatta kalma oranları açısından kritik öneme sahiptir. Bu çalışma ile makine öğrenmesi algoritmalarının kalp hastalığı teşhisindeki performansının Weka platformunda kapsamlı biçimde incelenmesi amaçlanmakta...

Metabolomics allows the analysis of metabolites in biological samples to identify biomarkers associated with metabolic processes, and among these volatile organic compounds (VOCs) have emerged as a significant component in non-invasive diagnostics playing a crucial role in understanding physiological and pathological conditions. The changes in meta...

Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data pois...

Cardiovascular disease (CVD) remains a leading global health threat, responsible for one in five deaths worldwide. Early detection is critical to mitigate morbidity and mortality, yet traditional diagnostic methods often rely on reactive clinical assessments, missing opportunities for preventive intervention. In this study, we developed a machine l...

In this work, the design and optimization of a surface plasmon resonance (SPR) biosensor is reported by examining the thickness of the silver (Ag) layer and the efficiency of refractive index fluctuations to detect biomolecules. In addition to evaluating the important parameters like sensitivity, quality factor (QF), signal-to-noise ratio (SNR), an...

This work tackles the growing hazard of pomegranate infections by utilizing deep learning for early detection and control. We created a strong classification system by implementing models with TensorFlow, Keras, and NumPy, as well as a user-friendly interface with Python and Streamlit. Five CNN architectures were evaluated: ResNet50, VGG16, DenseNe...

The study investigates the application of deep learning to diagnose eye diseases like glaucoma and diabetic retinopathy based on imaging with fundus images, 2D OCT, and 3D OCT imaging. We investigate state-of-the-art architectures, including Vision Transformers (ViT), Convolutional Neural Networks (CNN), and hybrid frameworks on various datasets, i...

Understanding cell adhesion and detachment is crucial for advancing disease diagnosis, treatment, and biomaterial development. Optical phase imaging techniques enable continuous, label-free observation of cells undergoing dynamic processes, including cell adhesion and detachment. To quantitatively study these processes with single-cell precision, a...

Objectives To evaluate carbon dioxide (CO2) footprint of celiac disease (CeD) diagnostic guidelines and follow‐up practices for children/adolescents. Methods Two‐hundred and thirty‐six patients diagnosed and followed up for CeD in Umbria region during 2020–2023 were included in this retrospective study. Patients were divided in two groups: Group 1...

Objective China is a country with a high burden of tuberculosis (TB). It is vital to reduce the number of new cases of TB in China. We aimed to examine and investigate the distribution and affecting factors of the latent tuberculosis infection (LTBI) detection rate in hospitalized patients in Suzhou, Jiangsu Province. Methods We analyzed the link...

Sexually transmitted diseases (STDs) are a significant global health concern, affecting millions of people worldwide. These diseases can lead to serious health complications, including infertility, cancer, and pregnancy problems. The most common STDs include gonorrhea, syphilis, chlamydia, trichomoniasis, and HIV. This review highlights the current...

Hyperspectral imaging (HSI) technology has great potential for the efficient and accurate detection of plant diseases. To date, no studies have reported the identification of yellow vein clearing disease (YVCD) in lemon plants by using hyperspectral imaging. A major challenge in leveraging HSI for rapid disease diagnosis lies in efficiently process...

Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study...

Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence alleviate some of the costly and tedious steps involved in traditional histochemical staining processes, permitting multiplexed staining and tissue preservation. However, potential hall...

Nanosensors and nanobiosensors are emerging as powerful tools for precision horticulture, enabling the real-time monitoring and control of crops at an unprecedented level of detail. These miniaturized sensors can detect a wide range of parameters, including temperature, humidity, pH, nutrient levels, pathogens, and more. By providing farmers with a...

Background Epigenetic aging measures or clocks are DNA methylation‐based indicators of biological aging, linked to health outcomes and disease risk. Physical activity and exercise may influence epigenetic aging, suggesting a pathway through which it promotes healthier aging and reduces chronic disease burden. In this study, we assessed the associat...

Iron is an essential requirement for normal cellular function and oxygen transport. Deficiency of iron, due to suboptimal intake, blood loss, malabsorption or maldistribution is the most common nutrient deficiency worldwide. Iron deficiency (ID) has traditionally been ignored until anemia develops. Amongst patients with cardiovascular (CV) disease,...

This case report explores the therapeutic potential of lymphovenous bypass (LVB) surgery performed at the neck in neurodegenerative diseases, specifically Alzheimer’s disease (AD) dementia. The subject is a 58-year-old woman who was previously healthy but began experiencing unexplained memory decline and frequent disorientation in the last 7 years,...

Background/Objectives: Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. Methods: This study presents a comprehensive deep learning (DL) framework that in...

Quick disease diagnosis is crucial in agriculture to reduce crop yield losses. Deep learning techniques have already been successfully applied to several tasks in agriculture, but it requires sufficient datasets. Obtaining sufficient plant disease images is challenging due to seasonality and a shortage of experts. Generative Adversarial Networks (G...

Pests and diseases seriously affect the quality and yield of maize. Therefore, it is important to carry out disease diagnosis and identification for timely diagnosis and treatment of maize pests and diseases and to improve maize production quality and economic efficiency. In this study, an improved Resnet50-based maize pest identification model was...

Objetivo: compreender o papel da equipe de enfermagem na melhora da saúde, bem-estar e qualidade de vida de populações vulneráveis com doenças crônicas. Método: Pesquisa bibliográfica desenvolvida através de revisão bibliográfica de literatura, com busca e seleção de estudos publicados nos últimos 10 anos. Resultados: A equipe de enfermagem tem pap...

Growing evidence suggests that specific volatile organic compound (VOC) profiles may reflect key pathophysiological processes in Parkinson’s disease (PD), including alterations in the microbiome, metabolism, and oxidative stress. Identifying reliable VOC biomarkers could enable non-invasive tests for early diagnosis, disease monitoring, and therapy...

La Organización Mundial de la Salud (OMS), reconoce que un gran número de personas que poseen un diagnóstico de Trastorno por Uso de Sustancias (TUS) además sufren de comorbilidades médicas y psiquiátricas (UNODOC, 2024). La encuesta Nacional sobre el Uso de Drogas y la Salud del Instituto Nacional de Salud (NIH) de los Estado Unidos reportó que, e...

Plants are crucial to society, the environment, and the economy but are susceptible to diseases that threaten agricultural productivity and food security. Early detection of plant diseases is essential to minimize crop losses. Traditional methods are often time-consuming and error-prone, particularly in the early stages. Recently, Artificial Intell...

Background Identifying the set of patients with a particular disease diagnosis across electronic health records (EHRs), referred to as a phenotype, is an important step in clinical research and applications. However, this task is often challenging, where incomplete data can render definitive classifications impossible. We propose a probabilistic ap...

Functional Magnetic Resonance Imaging (fMRI) is essential for studying brain function and diagnosing neurological disorders, but current analysis methods face reproducibility and transferability issues due to complex pre-processing and task-specific models. We introduce NeuroSTORM (Neuroimaging Foundation Model with Spatial-Temporal Optimized Repre...

Celiac disease (CeD) is a widely diffused chronic autoimmune disorder triggered by the ingestion of gluten, in genetically predisposed individuals. Small bowel capsule endoscopy (SBCE) plays a pivotal role as a noninvasive tool for diagnosing and monitoring CeD. This review aims to summarize the current and potential future role of SBCE in the fiel...

Background and Aims White Blood Cells (WBCs) are essential for immune defense against infections. Automated WBC identification from microscopic images aids in diagnosing diseases like leukemia and AIDS. However, the complexity of WBC morphology due to varying maturation stages and staining techniques complicates classification. This study aims to e...

Infectious diseases remain a leading global health concern, requiring rapid, precise, and cost‐effective diagnostic approaches. Traditional diagnostic methods, such as culture‐based techniques, serological assays, and molecular diagnostics, often have sensitivity, specificity, and time efficiency limitations. The emergence of mass spectrometry (MS)...

Background Phenylketonuria (PKU) is a rare disease. Children who are diagnosed with PKU often encounter psycho-behavioral difficulties, which can significantly impact quality of life and social integration. The aim of this study was to evaluate the prevalence of psycho-behavioral difficulties and explore potential factors associated with their occu...

Accurate segmentation of liver and liver tumors in medical imaging remains a critical challenge due to the complex anatomical structure of the liver and the heterogeneity of tumors. This study proposes a novel Modified U-Net (mU-Net) architecture that integrates object-dependent high-level features to enhance segmentation performance. The proposed...

The classification of oral diseases, particularly distinguishing between cancerous and non-cancerous lesions is a critical task for early diagnosis and treatment planning. In this study, we propose a deep learning-based approach that utilizes Inception ResNet V2 for feature extraction and a simple dense neural network for classification. To optimiz...

Near‐Infrared (NIR) detectors are crucial for applications like autonomous driving, food safety, and disease diagnosis. However, conventional photodetectors rely on complex epitaxial processes and costly single‐crystalline substrates yet still suffer from high dark current and limited detection performance at room temperature. Many of these detecto...

Background Hyperuricemia (HUA) is a metabolic disorder caused by an imbalance between uric acid (UA) production and excretion. It is closely associated with various diseases, including gout and kidney disease. The intestines play a significant role in UA excretion, and emerging evidence suggests that gut microbiota modulate UA excretion and degrada...

Background Despite the high suicide rate in South Korea, older adults are reluctant to see a psychiatrist. Recently, text mining has gained popularity to detect depression in social media posts, but older adults rarely use social media. However, more than 90% of them use smartphones. South Korea has also made a public effort to utilize a mobile app...

Parkinson’s disease (PD) is caused by dopamine neuron loss. Dopaminergic neurons in the “substantia nigra” which gradually die. Accurate and timely diagnosis is essential for treating Parkinson’s disease (PD). In this research, we present a unique method using Long Short-Term Memory and a 3D Convolutional Neural Network for Parkinson’s disease diag...

Climate change is increasingly disrupting medical laboratory operations worldwide, affecting diagnostic accuracy, infrastructure integrity, and supply chain stability. Hurricane Maria in 2017 devastated Puerto Rico, a major hub for medical supply manufacturing, leading to critical shortages of blood bags and reagents in U.S. hospitals. Rising globa...

Chiral molecular discrimination is critical for drug safety and disease diagnosis, yet discriminating amino acid enantiomers remains challenging due to the limitations of chiral selectors. Terahertz spectroscopy captures molecular structural vibrations with low photon energy, but conventional methods fail to resolve subtle chiral differences in bio...

Nanocatalysis has established as a transformative approach in healthcare, offering pioneering solutions for diagnostics and therapeutics. By leveraging the unique physicochemical properties of nanomaterials, nanocatalytic systems improve reaction proficiencies, enabling quick and highly sensitive recognition of biomarkers for disease diagnosis. Nan...

Clinical data classification became critical for medical decision-making support systems. While classification methods like multi-layer perceptrons have significantly contributed to disease diagnosis, their performance is often limited by the backpropagation algorithm’s susceptibility to local minima, slow convergence rates, and sensitivity to hype...

Surgical video understanding is pivotal for enabling automated intraoperative decision-making, skill assessment, and postoperative quality improvement. However, progress in developing surgical video foundation models (FMs) remains hindered by the scarcity of large-scale, diverse datasets for pretraining and systematic evaluation. In this paper, we...

Objective T2DM (type 2 diabetes mellitus) is a chronic metabolic disease that seriously affects human health. Abnormal expression of microRNAs has been reported to play an important role in disease diagnosis. This study aimed to investigate the predictive value of miR-4454 for T2DM and possible risk factors for comorbidity and complications (CC) of...

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many cri...

A technology that allows on-site targeting of odorants with high sensitivity and selectivity without using sophisticated instruments would have various useful applications from water quality testing to disease diagnosis. Here, we report a portable technology in which Sf21 cells expressing Drosophila melanogaster odorant receptors, co-receptors, and...

Optical Coherence Tomography (OCT) provides high-resolution, 3D, and non-invasive visualization of retinal layers in vivo, serving as a critical tool for lesion localization and disease diagnosis. However, its widespread adoption is limited by equipment costs and the need for specialized operators. In comparison, 2D color fundus photography offers...

Chronic diseases like the Chronic Kidney Disease (CKD) are silent in nature and therefore hard to diagnose in the early stages. There is a need for early detection so that intervention and treatment can be done effectively. This research proposes a new deep learning-based system which is set to be used in the early diagnosis of chronic diseases. In...

Early and accurate detection of Heart Disease (HD) is critical for improving patient outcomes, as HD remains a leading cause of mortality worldwide. Timely and precise prediction can aid in preventive interventions, reducing fatal risks associated with misdiagnosis. Machine learning (ML) models have gained significant attention in healthcare for th...

Deep learning has emerged as a transformative force in healthcare, revolutionizing diagnostics, treatment planning, and drug discovery through its ability to extract complex patterns from high-dimensional data. This paper provides a comprehensive review of Deep learning applications in medical imaging, genomics, and clinical decision-making, emphas...

The world is witnessing a significant rise in ocular diseases, affecting not only the elderly but also young adults and children due to the increasing use of electronic devices. In response, the goal of this research is to create an advanced method for the early identification of ocular diseases. The model used is EfficientNetB0 because it delivers...

Introduction This study evaluated the distribution characteristics, influencing factors, and future trends of non-Hodgkin lymphoma (NHL) burden in children and adolescents globally from 1990 to 2021. Methods Data were obtained from the Global Burden of Disease Study database. Multiple analytical methods were used, including Joinpoint regression, a...

Accurate nucleic acid quantification analysis (NQA) is crucial for disease treatment and prevention. However, existing digital NQA methods often lack sufficient automation and speed. In this study, we introduce a novel rapid and automated active‐matrix digital microfluidics‐based droplet digital recombinase polymerase amplification method (AM‐DMF‐d...

The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated...

The high‐resolution three‐dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense bre...

Progressive neurodegenerative diseases involve neuronal dysfunction from cellular to circuit to whole-brain levels, but complexity and variability, both between and within diseases, pose significant research challenges. However, although they are differentiated by anatomical origins, vulnerable neuronal subtypes, and specific misfolded proteins, ne...

The field of oral disease diagnosis and management has undergone significant transformation in recent years, propelled by innovations in diagnostic technologies, molecular biology, and interdisciplinary clinical approaches [...]

This article presents a comprehensive comparative analysis of functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG) in the diagnosis of psychiatric disorders. While fMRI has dominated neuroimaging research in psychiatry for decades, recent technological advancements have revitalised interest in EEG methodologies, particularl...

Background Current phenotype-based diagnostic tools often struggle with accurate disease prioritization due to incomplete phenotypic data and the complexity of rare disease presentations. Additionally, they lack the ability to generate patient-centered clinical insights or recommend further symptoms for differential diagnosis. Methods We developed...

We present Heartcare Suite, a multimodal comprehensive framework for finegrained electrocardiogram (ECG) understanding. It comprises three key components: (i) Heartcare-220K, a high-quality, structured, and comprehensive multimodal ECG dataset covering essential tasks such as disease diagnosis, waveform morphology analysis, and rhythm interpretatio...

Los grupos KidsTime o tiempo de los niños, son una modalidad de grupos multifamiliares dirigidos a personas con un diagnóstico de enfermedad mental y sus familiares, cuyos objetivos son minimizar el riesgo de transmisión generacional de la enfermedad mental, ofrecer un apoyo y comprensión de lo que es la enfermedad mental a los niños y adolescentes...

ERA25, Vienna, Austria

Nanofluidics can increase mass and heat transfer through various media to repair or damage cells, human organs, and tissues based on various technologies including magnetic hyperthermia treatment or active coatings. For all these reasons, there is a real need for a NanoSensor Network to detect infectious, especially incurable diseases today, to dis...

Deep learning models have shown remarkable success in dermatological image analysis, offering potential for automated skin disease diagnosis. Previously, convolutional neural network(CNN) based architectures have achieved immense popularity and success in computer vision (CV) based task like skin image recognition, generation and video analysis. Bu...

Malnutrition is a serious issue in developing countries. It affects about 165 million children under the age of five and causes about 13 million deaths annually. Among various nutritional deficiencies, protein deficiency in diets is a major contributing factor. One of the befitting ways to overcome malnutrition is to increase reliance on plant-base...

OREIGN body syndrome in sheep is a real threat. It causes several complications leading to economic loses. This work throws light on the most important clinicopathological alterations associated with foreign body syndrome in the sheep and proposes new biomarkers for its detection as well as its surgical intervention prognosis. Twenty apparently-hea...

Background We aimed to comprehensively analyze the application of machine learning (ML) in dentistry and oral surgery using bibliometric methods to identify research trends, hotspots, and future directions. Methods Publications related to ML in dentistry and oral surgery published between 2010 and 2024 were retrieved from the Science Citation Inde...

Periodontitis is a common and frequent oral disease which is characterized by persistent loss of periodontal supportive tissue and seriously affects the physical and psychological health. Existing clinical diagnostic indicators are incapable of accurately diagnosing periodontitis patients at an early stage, while traditional treatment methods are u...

Introduction: Chronic dermatoses significantly affect patients’ lives, and global burden of skin diseases represents important public health challenge worldwide, with the phenomenon of a tendency towards increasing level of dermatological pathology.Methods: In the last two decades, there has been a significant breakthrough in improving methods and...

The detection of biomarkers in interstitial fluid (ISF) holds significant promise for early disease diagnosis and monitoring. Porous silicon (pSi) offers a versatile platform for implantable biosensors due to its biocompatibility, high surface area, and tunable properties. This study presents the development and characterization of a label‐free pSi...

Staphylococcal enterotoxin B (SEB) holds critical importance in disease diagnosis, food safety, and public health due to its high toxicity and potent pathogenicity. Traditional immunoassay methods for detecting SEB often exhibit insufficient accuracy and robustness. This study leverages machine learning technology to integrate the quantitative meas...

Pyruvate kinase M2 (PKM2) is a critical enzyme regulating cell metabolism and growth under different physiological conditions. In its metabolic role, PKM2 catalyzes the final and also a rate-limiting reaction in the glycolytic pathway, converting phosphoenolpyruvate (PEP) to pyruvate while phosphorylating adenosine diphosphate (ADP) to pyruvate and...

Due to high performance and versatility, field-effect transistor (FET)–based sensors have gained attention in the growing interest in biosensor for early disease diagnosis and analysis. Sensor performance is greatly affected by transition materials such as silicon, graphene, carbon nanotubes, and metal-organic frameworks, because their unique elect...

With their ability to produce antibiotics, influence drug transport, and serve as vehicles or adjuvants for drug delivery, microbial signatures may provide new information on the pathophysiology of different lung illnesses. Most investigations of lung microbiome signatures were previously conducted using bronchoalveolar lavage (BAL) fluid and usual...

Alzheimer’s disease (AD) is a prevalent neurodegenerative disease that primarily affects the elderly population. The early detection of mild cognitive impairment (MCI) holds significant clinical importance for prompt intervention and treatment of AD. Currently, functional connectivity (FC) networks-based diagnostic methods for early MCI (eMCI) dete...

Objective To determine distinct patterns of patients with autoimmune diseases harbouring anti-Ku antibodies and their respective prognosis. Methods Anti-Ku-positive patients were retrieved through four immunology departments. Clusters were derived from unsupervised multiple correspondence analysis, not including the disease’s diagnosis, followed b...

Nucleolar small RNA (snoRNA), as a class of non-coding RNAs, play a crucial role in eukaryotic cells. They are widely involved in post-transcriptional modifications of ribosomal RNAs, including methylation and pseudouridylation, precisely regulating the process of ribosome biogenesis, ensuring the integrity of ribosome structure and function, and t...

Introduction: Fabry´s disease is a genetic disorder that courses with systemic and ocular manifestations, showing changes in the cornea, conjunctiva, and retina. Objective: To describe the retinal alterations identified in a patient diagnosed with Fabry´s disease in Rionegro, Antioquia in 2024, based on a case report. Case description: The ocular m...

The rapid and accurate quantitative analysis of cell chemotaxis, which is essential in biology, medicine, and drug development, enables the evaluation of the directional migration capability of cells and the simulation of in vivo cell chemotaxis. However, traditional methods for studying cell chemotaxis often depend on complex experimental procedur...

In situ tissue biopsy with an endoluminal catheter is an efficient approach for disease diagnosis, featuring low invasiveness and few complications. However, the endoluminal catheter struggles to adjust the biopsy direction by distal endoscope bending or proximal twisting for tissue sampling within the tortuous luminal organs, due to friction-induc...

Swarm intelligence optimization algorithms represent a significant branch of nature-inspired computational methods, designed to solve complex optimization problems by simulating the collective behavior of biological systems. Whale optimization algorithm (WOA) is a newly developed meta-heuristic algorithm, which is mainly based on the predation beha...