Pulmonology

RTsDEN: reverse task attention enabled deep learning model for lung cancer detection using computed tomography.

BACKGROUND: Lung cancer remains the most significant cause of cancer-related death worldwide due to the critical challenges in diagnosis. Despite the promising efforts, the existing models faced challenges in capturing the complex patterns in medical imaging data while minimizing the computational complexity. In this research, the lung cancer detection using Computed Tomography (CT) images is perf...

Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.

PURPOSE: To evaluate the diagnostic performance of two commercial artificial intelligence (AI) systems versus that of radiologists on routine clinical chest computed tomography (CT) and to identify the imaging characteristics that limit AI performance. MATERIALS AND METHODS: We retrospectively analyzed the 5-mm-slice chest CT of 102 patients (353 nodules or masses). The detection performance of tw...

Artificial Intelligence-Based Risk Prediction Models for Complications After Tongue Cancer Surgery.

IMPORTANCE: Glossectomy and reconstruction for tongue tumors carries substantial risk of postoperative morbidity, yet current tools offer limited indi...

Automated detection of marginal bone loss levels in implant brands using deep learning on periapical radiographs.

BACKGROUND: In contemporary dental practice, implants are the standard solution for edentulism. However, the wide variety of implant brands and the pr...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...

Effect of an electronic health record-integrated machine learning asthma risk marker on pediatrician prognostic accuracy during preschool age: a pilot randomized clinical trial.

Early identification of children at risk for persistent asthma is challenging because preschool respiratory symptoms are heterogeneous and often overl...

Comparative machine learning modeling of resting energy expenditure estimation in mechanically ventilated children after cardiac surgery.

OBJECTIVE: Nutrition status is vital for children's recovery following cardiac surgery, with substantial inter-individual variability in metabolic dem...

Large Language Models Provide Accurate but Potentially Unsafe Answers to Multimodal Critical Care Medicine Board Review Questions.

OBJECTIVES: To evaluate the performance of Chat Generative Pre-Trained Transformer-4 Omni (ChatGPT-4o) in answering multimodal critical care board rev...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

Integrating physical modeling with artificial intelligence for predicting fish survival zones in polluted rivers to maintain a sustainable aquaculture industry.

Water quality prediction and management are crucial for ensuring the sustainability of water supplies. Contaminated water can harm humans and aquatic ...

Automated classification of clinical T staging pulmonary oncology from chest CT reports: a natural language processing framework for real-world data utilization.

BACKGROUND: Clinical Tumor, Node, and Metastasis (cTNM) classification is vital for predicting treatment efficacy and prognosis in patients with cance...

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

BACKGROUND: Lung cancer ranks among the most lethal malignancies globally, and its traditional diagnosis suffers from strong subjectivity, high misdia...

REFLECTIVE-TIAB: cost-effective prompt optimisation for large language model-based title and abstract screening in literature reviews.

BACKGROUND: Title and abstract screening is a labor-intensive stage of systematic reviews. Large language models (LLMs) can automate this process, but...

Mechanism-Based Multitarget Modeling for Pathway-Level Prediction of PI3K/Akt Signaling Perturbation Induced by Liquid Crystal Monomers.

Liquid crystal monomers (LCMs) are emerging contaminants whose system-level toxicity mechanisms remain poorly understood. Here, we developed a pathway...

Patient centered and technology enabled strategies to improve Tuberculosis treatment adherence.

INTRODUCTION: Tuberculosis (TB) treatment adherence (how regular patients follow the prescribed medication) has a major role in TB control. In high bu...

Development of an Arterial Carbon Dioxide Estimation Model Using End-Tidal Carbon Dioxide Levels during Surgery in the Pediatric Population.

BACKGROUND: Intraoperative arterial carbon dioxide partial pressure monitoring is essential for pediatric ventilatory management but requires invasive...

REN: Anatomically-Informed Mixture-of-Experts for Interstitial Lung Disease Diagnosis.

Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However...

Plasma proteomics improves thrombosis prediction in patients with cancer and identifies targetable IL-17-driven endothelial activation.

Thrombosis remains a major cause of morbidity and mortality in patients with cancer. Existing risk models fail to reliably predict venous thromboembol...

RetiMap: Automated Retinal Vascular Measures Link Microvascular Structure to Metabolic Health and Predict Cardiovascular Risk.

Fundus imaging enables noninvasive, high-resolution visualization of the retinal microvasculature. Advances in artificial intelligence (AI) now allow ...

Modelling the fate of chemicals of emerging concern in wastewater treatment: From mechanistic models to hybrid data driven approaches.

Chemicals of emerging concern (CECs) pose major challenges for wastewater treatment plants because they are difficult to biodegrade, persist in the en...