Intensivists

Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence

Background: Delayed or missed diagnosis of congenital heart disease (CHD) contributes to excess pediatric mortality worldwide. Echocardiography (echo) is central to diagnosing and triaging CHD, yet expert interpretation remains a scarce and maldistributed global resource. Artificial intelligence (AI) offers the potential to democratize diagnostics and extend expert-level interpretation beyond larg...

A Hybrid Rule-Based and Deep Learning Framework for Ventilator Waveform Segmentation and Delineation

Accurate assessment of patient-ventilator interaction is critical for optimizing respiratory support and detecting harmful dyssynchronies linked to adverse outcomes, including ventilator-induced lung injury and prolonged ICU stays. This requires precise, breath-by-breath segmentation and phase delineation of ventilator waveforms, specifically pressure, flow, and volume. Current reliance on manual ...

Deep Learning Decodes Latent ECG Signatures of Stress Cardiomyopathy

Background Stress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management...

Clinical and Cross-Domain Validation of an LLM-Guided, Literature-Based Gene Prioritization Framework

Background: We previously published a literature based pipeline for sepsis gene prioritization (PS3 and candidate genes) using an LLM enabled retrieva...

Coarse-to-Fine Non-rigid Multi-modal Image Registration for Historical Panel Paintings based on Crack Structures

Art technological investigations of historical panel paintings rely on acquiring multi-modal image data, including visual light photography, infrared ...

Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling

The recent surge in popularity of Nano-Banana and Seedream 4.0 underscores the community's strong interest in multi-image composition tasks. Compared ...

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment

Sepsis remains one of the leading causes of mortality in intensive care units, where timely and accurate treatment decisions can significantly impact ...

GEOGRAPHIC DOMAIN SHIFT PRECIPITATES DIVERGENT FAILURE MODES IN DEEP LEARNING BASED TUBERCULOSIS SCREENING: A MULTI-NATIONAL EXTERNAL VALIDATION STUDY

Background: Deep learning algorithms for tuberculosis (TB) screening frequently achieve radiologist-level performance during internal evaluation, yet ...

Accuracy of Artificial Intelligence-Based Models versus Traditional Scoring Systems (APACHE, SOFA, SAPS) for Predicting Mortality in ICU Patients: A Systematic Review and Meta-Analysis

Introduction: Reliable estimation of mortality among critically ill patients is crucial for guiding clinical decisions and optimizing ICU performance....

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding

Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLM...

Achieving Expert-Level Clinical Infection Detection with LLMs from Clinical Documents: Validation in Complex Patient Cases with Cirrhosis

BackgroundSystemic infections are a leading cause of hospitalization and death among patients with cirrhosis. Timely and accurate infection identifica...

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs)...

Managing waste for production of low-carbon concrete mix using uncertainty-aware machine learning model.

This study introduces an uncertainty-aware AI-driven optimization framework for designing sustainable concrete mixtures that incorporate waste-derived...

Review of Feed-forward 3D Reconstruction: From DUSt3R to VGGT

3D reconstruction, which aims to recover the dense three-dimensional structure of a scene, is a cornerstone technology for numerous applications, in...

ViLU: Learning Vision-Language Uncertainties for Failure Prediction

Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new V...

ViLU: Learning Vision-Language Uncertainties for Failure Prediction

Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new V...

Stable-Hair v2: Real-World Hair Transfer via Multiple-View Diffusion Model

While diffusion-based methods have shown impressive capabilities in capturing diverse and complex hairstyles, their ability to generate consistent a...

Bridging Data Gaps of Rare Conditions in ICU: A Multi-Disease Adaptation Approach for Clinical Prediction

Artificial Intelligence has revolutionised critical care for common conditions. Yet, rare conditions in the intensive care unit (ICU), including rec...

Cooperative Mapping, Localization, and Beam Management via Multi-Modal SLAM in ISAC Systems

Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) ...

Multi-modal Representations for Fine-grained Multi-label Critical View of Safety Recognition

The Critical View of Safety (CVS) is crucial for safe laparoscopic cholecystectomy, yet assessing CVS criteria remains a complex and challenging tas...