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Clinical phenotyping of bloodstream infections: a review of current evidence
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Clinical phenotyping of bloodstream infections: a review of current evidence

Bloodstream infections (BSIs) are a leading cause of morbidity and mortality, yet their clinical heterogeneity continues to challenge effective patient stratification and treatment optimisation. In other heterogeneous conditions such as sepsis, data-driven clinical subphenotyping has identified reproducible subgroups with distinct outcomes and treatment responses. Whether similar approaches can be applied to BSIs to improve clinical management and trial design is an area of growing interest. We aimed to review t…

Scientific Reports
Noninvasive Profiling of the Glioma Vascular Microenvironment via 7T MRI: Decoding Angiogenic Signatures for Isocitrate Dehydrogenase and World Health Organization Grade Differentiation
Evidence-backed gain

Noninvasive Profiling of the Glioma Vascular Microenvironment via 7T MRI: Decoding Angiogenic Signatures for Isocitrate Dehydrogenase and World Health Organization Grade Differentiation

Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microenvironment promotes tumor progression. A noninvasive screening tool capable of mapping tumor vascularity may assist in preoperative grading and subtyping of gliomas. To evaluate 7T susceptibility-weighted imaging (SWI) for differentiating glioma isocitrate dehydrogenase (IDH) status and World Health Organization (WHO) grade based on vascular microenvironment features. Retrospective. Among 218 patients wit…

EndoVLM: A Vision-Language Assistant for Gastrointestinal Endoscopy
Evidence-backed gain

EndoVLM: A Vision-Language Assistant for Gastrointestinal Endoscopy

Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for efficient and accurate computer-aided diagnosis of gastrointestinal diseases. To address this, we propose EndoVLM (Endoscopy Vision-Language Model), a specialized visual question-answering assistant for gastroenterology. EndoVLM introduces ConvNeXt as a hierarchical visual encoder to replace traditional ViTs (Vision Transformers), inherently compressing high-resolution gastrointestinal endoscopy images in…

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images
Evidence-backed gain

Deep Learning-Based Classification of NIFTP and Invasive Encapsulated Follicular Variant of Papillary Thyroid Carcinoma Using Gross Pathology Images

Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulated follicular variant of papillary thyroid carcinoma (IEFVPTC) are diagnostically challenging thyroid neoplasms with overlapping clinical and molecular characteristics. Although artificial intelligence has shown promise for diagnostic support in radiology and histopathology, its application to gross pathology remains unexplored. This study analyzed gross pathology photographs from 87 patients (43 with NIF…

Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation
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Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation

Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates m…

Health

Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire

The adaptive immune receptor repertoire (AIRR) - the collection of an individual's B-cell and T-cell receptors (BCRs and TCRs, respectively) - encodes cumulative immune history and is shaped by both germline genetics and environmental exposures. Skewed repertoires have been linked to infections, vaccination responses and autoimmune diseases such as celiac disease (CeD) where biased usage of immunoglobulin and T-cell receptor genes reactive to disease relevant antigens has been reported. Motivated by evidence tha…

Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire
Health

Pulmonary Artery-to-Vein Volume Difference: A New Imaging Biomarker for Risk Stratification in Acute Pulmonary Embolism

Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (PAVVD), and evaluate its efficacy in risk stratification of acute pulmonary embolism (APE) compared with traditional computed tomography pulmonary angiography (CTPA) parameters, as well as the impact of chronic pulmonary disease (CPD). Materials and methods This retrospective study included 134 patients with APE (high/intermediate-high risk, n = 46; intermediate-low/low risk, n = 88) from April 2023 to Ma…

Pulmonary Artery-to-Vein Volume Difference: A New Imaging Biomarker for Risk Stratification in Acute Pulmonary Embolism
Health

International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing Western and Eastern Databases

Rationale and objectives The international application of artificial intelligence (AI) for lesion detection based on digital breast tomosynthesis (DBT) is limited due to disease variations among populations. We hypothesized that lesion detection models trained on either the Western or Eastern DBT dataset would exhibit reduced performance on another dataset. We proposed transfer learning to enhance lesion detection across DBT databases. Materials and methods The Western database (94 patients) was obtained from th…

International Application of Artificial Intelligence for Lesion Detection on Digital Breast Tomosynthesis: Comparing Western and Eastern Databases
Health

Predicting Synchronous Liver Metastasis in Pancreatic Cancer Using CT Radiomics and Clinical Features: A Machine Learning Approach

Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis (LM) in pancreatic cancer (PC), we developed and validated machine learning models integrating clinical and computed tomography (CT) radiomics features, and compared the performance and interpretability of linear (linear discriminant analysis [LDA]) versus nonlinear (multilayer perceptron [MLP]) architectures. Materials and methods This retrospective study enrolled 340 patients with pancreatic ductal aden…

Predicting Synchronous Liver Metastasis in Pancreatic Cancer Using CT Radiomics and Clinical Features: A Machine Learning Approach
Health

Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey

Objectives To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, and experiences regarding medical artificial intelligence (AI), and to explore and compare the factors influencing AI behavioral intention (BI; willingness to adopt AI) between physicians and nurses using the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and methods A nationwide cross-sectional survey was conducted among professionals in oncol…

Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey
Health

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…

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

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…

Deep Learning for Synthetic Postcontrast T1-Weighted MRI: A Systematic Review With Targeted Meta-Analysis of Brain Tumor Studies