Pulmonology

Measuring the Impact of AI on Report-Drafting Efficiency in Chest Computed Tomography Interpretation: Retrospective Analysis.

BACKGROUND: Artificial intelligence (AI), particularly deep learning, has shown promise in enhancing medical image interpretation and improving radiologists' efficiency. In China, growing imaging demand and workforce shortages have placed increasing pressure on radiology services. However, evidence on the operational impact of AI on reporting efficiency remains limited. OBJECTIVE: This study aimed...

Diagnostic accuracy of AI-assisted chest radiographs in tuberculosis screening: A Ghanaian clinical study.

BACKGROUND: Tuberculosis remains a major global health challenge, particularly in resource-limited settings where access to expert radiological interpretation is constrained. Artificial intelligence offers a promising solution to enhance diagnostic accuracy and efficiency in TB screening. AIM: This study aimed to evaluate the diagnostic performance of an AI-based system compared to a radiologist i...

Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use.

BACKGROUND: Postoperative opioid use has the risk of dependence and diversion. We developed an opioid-sparing regimen and identified factors associate...

Machine learning predicts sepsis deterioration trajectories.

Sepsis has heterogeneous clinical trajectories, but conventional severity scores offer only static risk estimates. Timely, dynamic prediction could en...

Characterizing heterogeneity and subphenotyping acute respiratory distress syndrome with computed tomography.

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome rather than a single disease. Patients who meet the same diagnostic cr...

Molecularly Engineered SERS Platform with Microturbulence-Enhanced Electrohydrodynamics for Multiplexed Profiling of Lung Cancer ctDNA.

Precise profiling of multiple circulating tumor DNA (ctDNA) is hindered by the challenge of resolving single-nucleotide variants (SNVs) and their trac...

Computational Discovery of MERS-CoV Main Protease Inhibitors Through Screening and Molecular Dynamics Simulations.

Targeting the main protease (Mpro) of coronaviruses has emerged as a promising therapeutic strategy for combating viral infections. Despite the global...

Explainable machine learning for long-term cardiovascular disease risk prediction in Chinese middle-aged and older adults: a 9-year longitudinal cohort study with web-based risk calculator.

Cardiovascular disease represents the leading cause of mortality in China, accounting for over 40% of all deaths. Existing risk prediction models pred...

Systems-level integration of machine learning, SHAP explainability, PCA, and network topology reveals multitarget protective actions of tannic acid against doxorubicin toxicity in pentose phosphate pathway.

Doxorubicin (DOX) is an effective chemotherapeutic agent; however, its clinical use is limited due to dose-dependent toxicities. This study aimed to i...

A Multi-Scale Hybrid Efficient Deep Learning Model for COPD Detection Using Respiratory Sounds.

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease, and early diagnosis is crucial for timely intervention and improved p...

Machine learning for mortality prediction using nutritional and inflammatory markers in critically ill patients.

INTRODUCTION: Malnutrition and muscle loss are key determinants of outcomes in critically ill patients, yet conventional ICU mortality scores (e.g., A...

Operational building datasets from multipurpose buildings in Denmark and Switzerland: High-resolution energy use and room measurements.

The dataset comprises Building Management System (BMS) data from an educational building located on the main campus of Aalborg University in Denmark, ...

AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trial.

Prioritizing artificial intelligence (AI)-detected imaging findings may reduce the time to diagnosis of lung cancer. This prospective, multicentre, ra...

Overall risk of cancer incidence attributable to adult body CT examinations: impact of a seven-year continuous quality improvement program.

OBJECTIVES: To estimate the impact of a continuous dose reduction and quality improvement program on radiation-induced cancer risk in adult computed t...

Genome-wide detection of human 5' UTR variants that impact protein translation.

The 5' untranslated region (5' UTR) of messenger RNAs (mRNAs) plays a central role in regulating protein synthesis initiation, particularly through th...

Identifying Undiagnosed COPD Using Machine Learning-Based Classification.

BACKGROUND: COPD is a leading cause of morbidity and mortality worldwide. However, 80% of individuals with COPD are undiagnosed, leading to accelerate...

3d-Transition Metal Doped Axially Coordinated Phthalocyanine Molecules as Highly Active Electrocatalysts for Oxygen Evolution/Reduction Reactions: A Machine Learning and DFT Study.

The electrocatalytic oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) are key processes in renewable energy technologies, which are...

Integrated Implementation Strategies to Promote the Use of AI-Assisted Diagnostic Software for Lung Nodule Screening in China: Process Evaluation Based on the RE-AIM Framework.

BACKGROUND: While artificial intelligence (AI)-assisted diagnostic software holds promise for improving diagnostic efficiency and reducing disparities...

Automatic Multi-Class Classification of 3D Relationships Between the Mandibular Third Molar and Canal on CBCT Using a Geometry-Aware Network.

OBJECTIVES: Accurate three-dimensional (3D) evaluation of the spatial relationship between the mandibular third molar (M3) and the mandibular canal (M...

HCLmNet: A unified hybrid continual learning strategy multimodal network for lung cancer survival prediction.

Lung cancer survival prediction remains one of the most challenging tasks in modern healthcare, as accurate and adaptive prediction models are essenti...