Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.

Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.

Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation.

A Novel Point-of-Care Prediction Model for Steatotic Liver Disease: Expected Role of Mass Screening in the Global Obesity Crisis.

Background/aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD.

Potent neutralization by a RBD antibody with broad specificity for SARS-CoV-2 JN.1 and other variants.

SARS-CoV-2 continues to be a public health burden, driven in-part by its continued antigenic diversification and resulting emergence of new variants. By increasing herd immunity, current vaccines have improved infection outcomes for many. However, prophylactic and treatment interventions that are not compromised by viral evolution of the Spike protein are still needed.

Leadership Lingo: Developing a Shared Language of Leadership Behaviors to Enrich Debriefing Conversations.

Introduction: Leadership in medical emergencies is variable and frequently suboptimal, contributing to poor patient care and outcomes. Simulation training can improve leadership in both simulated practice and real clinical emergencies. Thoughtful debriefing is essential.

The outcome of restrictive cardiac allograft physiology in severe coronary allograft vasculopathy.

Background: Microvascular dysfunction after heart transplantation leads to restrictive cardiac allograft physiology (RCP), which is classified as severe coronary allograft vasculopathy (CAV); however, the prognosis of RCP remains unclear. Therefore, in this study, we aimed to elucidate the prognosis of RCP in comparison with that of severe angiographic CAV.

Methods: We assessed 116 patients with severe CAV who underwent heart transplantation between 2004 and 2023.

SARS-CoV-2 ferritin nanoparticle vaccines produce hyperimmune equine sera with broad sarbecovirus activity.

The rapid emergence of SARS-CoV-2 variants of concern (VoC) and the threat of future zoonotic sarbecovirus spillover emphasizes the need for broadly protective next-generation vaccines and therapeutics. We utilized SARS-CoV-2 spike ferritin nanoparticle (SpFN), and SARS-CoV-2 receptor binding domain ferritin nanoparticle (RFN) immunogens, in an equine model to elicit hyperimmune sera and evaluated its sarbecovirus neutralization and protection capacity. Immunized animals rapidly elicited sera with the potent neutralization of SARS-CoV-2 VoC, and SARS-CoV-1 pseudoviruses, and potent binding against receptor binding domains from sarbecovirus clades 1b, 1a, 2, 3, and 4.

Characteristics of Medical Quality in Tertiary Traditional Chinese Medicine Hospitals by TOPSIS and RSR Methods.

Performance evaluation is important for improving medical quality and services. But, there is a lack of research for medical quality in traditional Chinese medicine (TCM) hospitals. This study examines the medical quality and various indicators of tertiary public traditional Chinese medicine hospitals in Gansu Province, to establish a foundation for improving the medical and management standards of these hospitals.

Three-Dimensional Quantitative Analysis of Internal Limiting Membrane Peeling Related Structural Changes in Retinal Detachment Repair.

Purpose: To assess macular microstructural changes associated with internal limiting membrane peeling (ILMP) using 3-dimensional optical coherence tomography (3D-OCT) in primary macula-off rhegmatogenous retinal detachment (RRD) repairs with vitrectomy and silicone oil (SO) tamponade.

Design: Retrospective, consecutive, interventional case series.

Methods: Setting: Institutional practice.

Hepatic Insulin Resistance Increases Risk of Gallstone Disease in Indigenous Americans in the Southwestern United States.

Introduction: Animal models indicate that hepatic insulin resistance (IR) promotes cholesterol gallstone disease (GSD). We sought to determine whether hepatic and whole-body IR is associated with incident GSD.

Methods: At baseline, 450 Southwestern Indigenous American adults without GSD were included.

Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions.

Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities.

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval.

Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction.

Background: Evaluating left ventricular diastolic function (LVDF) is crucial in echocardiography; however, the complexity and time demands of current guidelines challenge clinical use. This study aimed to develop an artificial intelligence (AI)-based framework for automatic LVDF assessment to reduce subjectivity and improve accuracy and outcome prediction.

Methods: We developed an AI-based LVDF assessment framework using a nationwide echocardiographic dataset from five tertiary hospitals.

A Case-based Guide for World Health Organization (WHO) Grade 2 Meningioma Radiosurgery and Radiation Therapy from The Radiosurgery Society.

Purpose: Meningiomas represent the most common primary tumor of the central nervous system. Current treatment options include surgical resection with or without adjuvant radiation therapy (RT), definitive RT, and observation. However, the radiation dose, fractionation, and margins used to treat patients with WHO grade 2 meningiomas, which account for approximately 20% of all meningiomas, are not clearly defined, and deciding on the optimal treatment modality can be challenging owing to the lack of randomized data.

Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction.

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%).

A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults.

Discovery of RXFP2 genetic association in resistant hypertensive men and RXFP2 antagonists for the treatment of resistant hypertension.

Hypertension remains a leading cause of cardiovascular and kidney diseases. Failure to control blood pressure with ≥ 3 medications or control requiring ≥ 4 medications is classified as resistant hypertension (rHTN) and new therapies are needed to reduce the resulting increased risk of morbidity and mortality. Here, we report genetic evidence that relaxin family peptide receptor 2 (RXFP2) is associated with rHTN in men, but not in women.

Phase 2 study of neoadjuvant enzalutamide and paclitaxel for luminal androgen receptor-enriched TNBC: Trial results and insights into "ARness".

Luminal androgen receptor (LAR)-enriched triple-negative breast cancer (TNBC) is a distinct subtype. The efficacy of AR inhibitors and the relevant biomarkers in neoadjuvant therapy (NAT) are yet to be determined. We tested the combination of the AR inhibitor enzalutamide (120 mg daily by mouth) and paclitaxel (80 mg/m weekly intravenously) (ZT) for 12 weeks as NAT for LAR-enriched TNBC.