± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
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877 published stories · page 2 of 59

Evidence-backed gain

An Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning

The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area has proven to yield similar accuracy levels to those obtained by dermatologists in different studies. Nevertheless, many challenges are still present, including underperformance and poor generalization in some cases, as well as low interpretability related to the use of black box models. This creates major obstacles for practical implementation since it requires explainability in addition to accurate dia…

Journal of Visualized Experiments · Health

An Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning
Deep Learning for Synthetic Postcontrast T1-Weighted MRI: A Systematic Review With Targeted Meta-Analysis of Brain Tumor Studies
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Deep Learning for Synthetic Postcontrast T1-Weighted MRI: A Systematic Review With Targeted Meta-Analysis of Brain Tumor Studies

BACKGROUND . Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) methods have emerged as a potential approach for synthesizing postcontrast T1-weighted images from precontrast sequences alone. OBJECTIVE . The objective of the present study was to systematically review DL-based synthesis of postcontrast T1-weighted MRI, characterize model architectures and evaluation practices across subspecialties, and perform targeted meta-analysis where sufficient literature existed. E…

Health
Separation of coeluted compounds in capillary gas chromatography: customization via machine learning retention time prediction
Evidence-backed gain

Separation of coeluted compounds in capillary gas chromatography: customization via machine learning retention time prediction

Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent determination. However, gas chromatography compound identification is not directly feasible unless the method is coupled with complementary techniques such as mass spectrometry or with referencing methods like retention indices. Statistical retention time prediction of compounds based on the gas chromatography experimental and instrumental parameters could facilitate the gas chromatography characterization…

Science
AI-ICE Guided Pulsed Field Ablation of Atrial Fibrillation with Variable Loop Circular Catheter
Evidence-backed gain

AI-ICE Guided Pulsed Field Ablation of Atrial Fibrillation with Variable Loop Circular Catheter

Background Intracardiac echocardiography (ICE) facilitates left atrial (LA) reconstruction during atrial fibrillation (AF) ablation. The artificial intelligence-based CARTOSOUND FAM (AIFAM) module enables automated three-dimensional LA reconstruction without the need for a dedicated pre-ablation mapping catheter. While this workflow has been described previously in radiofrequency ablation, its application and outcome in pulsed field ablation (PFA) remains limited. Objective To evaluate the feasibility, safety, a…

Health
Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus
Evidence-backed gain

Intramuscular Fat Assessed by AI-Assisted Muscle Ultrasound: Association With Cardiometabolic Risk Factors and Diabetic Nephropathy in Diabetes Mellitus

Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiometabolic outcomes. Artificial intelligence (AI)-assisted ultrasound of the rectus femoris (RF) offers a non-invasive approach for quantifying IMF. This study evaluated the association of IMF with diabetes-related complications (particularly diabetic nephropathy) and metabolic risk factors in patients with diabetes mellitus (DM). Materials and methods In this cross-sectional study, outpatients from a tertia…

Health
Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs
Evidence-backed gain

Stacked Ensemble Deep Learning Models for Accurate Detection and Size Stratification of Periapical Lesions on Intraoral Radiographs

Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition and reader variability. This study developed stacked deep learning ensembles for automated detection and radiographic size stratification of periapical lesions. In total, 146 radiographs comprising normal cases and three lesion-size categories were cropped around the root apex and augmented using predefined transformations. Five convolutional neural network backbones were trained, and their probability out…

Health

Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inherently restricts its reproducibility and global accessibility. Robotic ultrasound systems (RUSS) have evolved over the past 2 decades to mitigate these limitations by mechanically decoupling the human operator from the patient. This comprehensive review examines the historical trajectory of medical ultrasonography and robotics, highlighting their convergence into modern RUSS. We detail the taxonomies of ro…

Health
Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction

Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally developed for patients with type 2 diabetes, to evaluate its generalizability in predicting 1-year hospitalization risk in a disease-agnostic adult cohort, with attention to fairness and explainability. Materials and methods The study utilized de-identified electronic health record data from a retrospective cohort of 17 857 adult patients at the University of Florida Health. The original iPsRS framework wa…

Health
Adaptation of a fair individualized polysocial risk score for hospitalization risk prediction

Diagnosing melioidosis and tracking treatment outcomes using breath

Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients' breath that can aid in diagnosing meli…

Health
Diagnosing melioidosis and tracking treatment outcomes using breath

Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical diagnosis, where class imbalance and non-linear decision boundaries are common. Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Cl…

Health
Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) class…

Climate
Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

AI-powered medicinal chemistry and translational drug development

Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into approved medicines remains slow, expensive, and prone to high attrition across the pipeline from target identification to clinical validation. Artificial intelligence (AI) is beginning to reshape this landscape by enabling large-scale integration, interpretation, and generation of chemical, biological, and clinical data for hypothesis generation, chemical space exploration, and iterative cycles of model-guided…

Health
AI-powered medicinal chemistry and translational drug development

The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective

Aim To analyze the integration of Artificial Intelligence in nursing through the lens of the Fundamentals of Care framework. Design A discursive paper. Methods This discursive paper synthesizes current literature and theoretical perspectives to examine the Relationship, Integration and Context dimensions of the Fundamentals of Care framework in the era of Artificial Intelligence. Results Artificial Intelligence offers substantial benefits in optimizing workflow (Context) and clinical precision (Integration) thro…

Health
The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective

Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models

Objective This study seeks to explore the utility of social determinants of health (SDoH) variables in suicide prediction models. We aim to assess the impact of individual- and geographic-level SDoH factors on improving the performance of suicide prediction models and the identification of individuals at high risk for suicide. Methods A retrospective sample of 1214 deaths by suicide and 815,544 living patients was identified in the Maryland Suicide Data Warehouse (MSDW) and linked to census tract data through ge…

Health
Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models