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112 articles for “Diagnostic tool”
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Fundamental Principles of Fluid Behavior and Emerging Trends in Modern Fluid Mechanics Research
Abstract: Fluid behavior forms the foundation of numerous engineering technology and scientific applications, including aerospace flows, energy systems, and environmental processes. This paper presents a comprehensive overview of the fundamental principles governing fluid behavior, with a strong emphasis on their relevance to recent trends in fluid mechanic’s research. Core concepts such as fluid statics, fluid dynamics, and conservation laws are discussed to establish a solid theoretical framework. The study further examines …
Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 1, 2026 · pp. 14–21 Read article
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Photonic Diagnostics: Harnessing Optical Sensing for Non-Invasive Assessment of Coronary Obstruction
Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, with coronary artery blockages primarily atherosclerosis representing a critical challenge. The gold standard for diagnosing coronary artery disease remains invasive coronary angiography, a procedure that, while precise, carries inherent patient risks, high costs, and logistical burdens. Optical sensors, leveraging the principles of light-tissue interaction, offer real-time, high-resolution insights into vascular health, paving the way for early detection of arterial stenoses …
Published in International Journal of Optical Innovations & Research · Vol. 4, Issue 1, 2026 · pp. 25–30 Read article
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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Leveraging AI and Machine Learning for Early Prediction and Prevention of Non- Communicable Diseases in Resource-Limited Settings
Abstract: Populations in these regions face persistent structural barriers, such as underdeveloped healthcare infrastructure, shortages of trained health professionals, and fragmented or incomplete health information systems. These limitations delay timely diagnosis, restrict access to preventive care, and compromise effective disease management. In recent years, rapid progress in artificial intelligence (AI) and machine learning (ML) has opened promising avenues to mitigate these challenges. Practical applications already emerging include mobile health platforms for …
Published in Journal of AYUSH: Ayurveda, Yoga, Unani, Siddha and Homeopathy · Vol. 15, Issue 1, 2026 · pp. 9–15 Read article
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A Comprehensive Review on Male Infertility
Abstract: The failure to attain a clinical pregnancy naturally after one year of unprotected intercourse is termed as infertility. Some of the factors that contribute to Infertility are late marriages, advanced age, genetics and lifestyle to name a few. To tackle this scenario, infertile and sub fertile couples take the aid of assisted reproductive technology. Intra uterine insemination (IUI), In vitro fertilization (IVF), Intra cytoplasmic sperm injection (ICSI), surgical sperm retrieval …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 15, Issue 3, 2025 · pp. 95–103 Read article
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Computer Aided Diagnosis of Breast Cancer using Machine Learning Techniques
Abstract: Breast cancer is one of the significant health problems that lead to early mortality in women, especially those between 40 and 55 years of age all over the world. In recent years, the number of breast cancer cases among women has risen significantly, making early and accurate diagnosis more important than ever. Computer-aided diagnostic (CAD) tools have become valuable in supporting radiologists by enhancing the precision of breast cancer detection. …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 2, 2025 · pp. 1–11 Read article
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Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction
Abstract: Deep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system's systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 2, 2025 Read article
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Adoption of Artificial Intelligence in Periodontal Diagnostics: Awareness, Confidence, and Barriers Among Dental Practitioners in India
Abstract: AI has emerged as a transformative tool in healthcare, including periodontics, where it aids in diagnosing periodontal diseases, assessing bone loss, and predicting disease progression. Despite its potential, the adoption of AI in dentistry, particularly in India, remains limited. This study aimed to evaluate the awareness, confidence, and willingness of dental practitioners to adopt AI-based tools in periodontal diagnostics. A cross-sectional survey was conducted among 106 dental practitioners, including general …
Published in Research and Reviews: A Journal of Dentistry · Vol. 16, Issue 3, 2025 · pp. 27–38 Read article
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Advances in Nanotechnology-based Biosensors: Enhancing Sensitivity and Specificity in Biomedical Diagnostics
Abstract: This study provides an in-depth exploration of the rapidly evolving field of nanotechnology-based biosensors, emphasizing their significant impact on biomedical diagnostics. The integration of cutting-edge nanomaterials, including nanoparticles, nanowires, and quantum dots, has catapulted biosensing technology to new heights, yielding unprecedented gains in sensitivity and specificity, and revolutionizing the detection of biomolecules. The study highlights various types of nanobiosensors, including optical, electrochemical, and magnetic, each offering unique advantages for detecting …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 26, Issue 2, 2024 · pp. 26–32 Read article
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A Study of Optical Sensor in Clinical applications
Abstract: The escalating demand for precise, real-time, and minimally invasive diagnostic and monitoring tools in clinical practice has propelled the development of sophisticated sensor technologies. Among these, optical sensors have emerged as a cornerstone, leveraging the interaction of light with biological matter to translate molecular or cellular events into quantifiable signals. Their inherent advantages – including high sensitivity, specificity, rapid response times, non-ionizing nature, and potential for miniaturization – make them …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 1–7 Read article
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AI-Based Preventive Healthcare Using Quantum Computing
Abstract: With its improved performance and capabilities, quantum machine learning (QML) is becoming a promising field, especially in the healthcare industry for tasks like early heart disease prediction. In this work, a Quantum Support Vector Classifier (QSVC) is proposed as the basic classifier for a bagging ensemble learning model. Shapley Additive explanations (SHAP) are used to evaluate the significance of each attribute in the predictions in order to improve explainability. Using …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 15, Issue 2, 2025 Read article
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Early Lung Cancer Prediction using deep Learning
Abstract: Lung cancer is a global killer because it’s often found late. Finding it early is key to treatment and survival so computer assisted diagnostics are essential. This research uses deep learning to spot early stage lung cancer from CT scans. We trained and fine-tuned three convolutional neural networks—ResNet50, Dense Net 201 and EfficientNet-B0—using transfer learning. We preprocessed the lung CT images by resizing, normalizing and augmenting them to enhance the …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 2, 2026 Read article
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Advancements in AI-Driven Diagnostics for Dental Health: A Comprehensive Review
Abstract: Dental diseases, also known as oral diseases or dental conditions, encompass a range of health problems affecting the teeth, gums, mouth, and associated structures. These conditions can lead to pain, discomfort, and severe complications if left untreated. Early detection and accurate diagnosis are crucial for effective treatment and prevention of further complications. This comprehensive literature review aims to identify common dental problems such as Tooth Decay (Cavities), Gingivitis, Periodontitis, and …
Published in Current Trends in Signal Processing · Vol. 14, Issue 2, 2024 · pp. 1–7 Read article
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Lab Reagents and Their Importance in Bio-chemistry
Abstract: Biochemical reagents are essential substances in the study of life at the molecular level. These reagents help scientists detect, identify, and quantify bio-molecules such as proteins, carbohydrates, nucleic acids, and enzymes in biological systems. They play a vital role in understanding metabolic reactions and physiological processes within living organisms. Biochemical reagents are generally classified into analytical, diagnostic, enzymatic, chromogenic, and buffer reagents, each serving a specific purpose in laboratory and …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 16, Issue 1, 2026 Read article
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Intelligent Medical Devices and Robotics in Modern Healthcare: Technological Advancements and Economic Considerations
Abstract: The integration of robots and intelligent medical devices in intensive care units (ICUs) represents a significant advancement in healthcare technology. These systems, including robotic assistants, automated monitoring tools, and AI-powered diagnostic devices, are designed to enhance patient care, streamline workflows, and reduce human error. Robots in the ICU can assist with routine tasks such as medication delivery, patient repositioning, and even basic surgeries, enabling healthcare professionals to focus on critical …
Published in Journal of Advancements in Robotics · Vol. 11, Issue 3, 2024 · pp. 18–27 Read article
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Electrochemical Biosensors for Disease Diagnostics
Abstract: Electrochemical biosensors have emerged as one of the most promising tools for disease diagnosis because they combine high sensitivity, rapid response, portability, and low cost in a single analytical platform. In recent years, growing interest in early disease detection has pushed researchers to develop biosensors that can detect clinically important biomarkers in blood, saliva, urine, sweat, and other biological fluids. These sensors work through the interaction between a biological recognition …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 28, Issue 2, 2025 Read article
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CRISPR, Recombinant DNA, and LAMP-Based Platforms for Monkeypox: Emerging Tools for Diagnosis and Therapeutic Development
Abstract: Monkeypox, a zoonotic viral infection caused by the Monkeypox virus of the Orthopoxvirus genus, has recurred as a worldwide public health concern after recent outbreaks outside of its classical endemic areas in Central and West Africa. The resurgence has demanded a thorough reassessment of its epidemiology, mode of transmission, and clinical presentation. In light of these challenges, recombinant DNA technology (rDNA) has emerged as a candidate for developing vaccines, diagnostics, …
Published in International Journal of Virus Studies · Vol. 3, Issue 1, 2026 · pp. 7–28 Read article
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article
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Early Autism Diagnosis: Machine Learning Models and Their Effectiveness
Abstract: Diagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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Analysis of White Matter, Gray Matter, and Cerebrospinal Fluid Alterations in Neurological Disorders: A Deep Learning Approach
Abstract: This paper investigates the role of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) alterations in the pathophysiology of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, schizophrenia, and epilepsy. By leveraging advanced deep learning methodologies, we aim to automate the segmentation and analysis of brain structures from MRI scans, enabling a more detailed and precise evaluation of their roles in disease progression. These techniques allow for the identification …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 3, 2024 · pp. 21–27 Read article