Latest AI and machine learning research in pulmonology for healthcare professionals.
Lung cancer is characterized by profound intratumoral and inter-patient heterogeneity, spanning histological subtypes, molecular landscapes, and the tumor microenvironment. While multi-omics integration is essential for capturing this complexity, leveraging these data to explicitly define survival-associated subpopulations remains a significant challenge. In this study, we developed NeuroMDAVIS-FS...
Early detection of lung cancer in chest radiographs (CXRs) is crucial for improving patient outcomes, yet nodule detection remains challenging due to their subtle appearance and variability in radiological characteristics like size, texture, and boundary. For robust analysis, this diversity must be well represented in training datasets for deep learning based Computer-Assisted Diagnosis (CAD) syst...
Deep learning-based automated diagnosis of lung cancer has emerged as a crucial advancement that enables healthcare professionals to detect and initia...
Background: Intrinsic capacity (IC) is a key marker of healthy ageing, which captures an individuals physical and mental capacities, measured across f...
Pediatric asthma exacerbations are a frequent cause of emergency department (ED) visits and hospitalizations, yet accurate risk prediction remains lim...
Transcription factors recognise and bind specific DNA sequence patterns in promoters and enhancers thereby regulating gene expression. Variations in t...
Common genetic variants contribute to risk for complex human diseases. However, despite thousands of associations, variants modulating disease risk an...
The rapid progress of multimodal large language models (MLLMs) has led to increasing interest in agent-based systems. While most prior work in medical...
When biological foundation models such as scGPT and Geneformer process single-cell gene expression, what geometric and topological structure forms in ...
Introduction: Kidney biopsy reports contain rich information that is clinically actionable and useful for research. However, the narrative format hind...
Abstract Background Tuberculosis (TB) remains a major public health challenge in Nepal, with incidence rates substantially higher than global estimate...
Tuberculosis (TB) is the worldwide leading infectious killer due to a single pathogen and increasing antimicrobial resistance (AMR) makes it imperativ...
While deep learning has demonstrated considerable promise in computer-aided diagnosis for pulmonary embolism (PE), practical deployment in Computed To...
The image purification strategy constructs an intermediate distribution with aligned anatomical structures, which effectively corrects the spatial mis...
Objective: Quantitative computed tomography (QCT) can automatically quantify parenchymal abnormalities on chest CT imaging using deep learning. We lev...
Objective: This study investigates whether incorporating physiological coupling concepts into neural network design can support stable and interpretab...
Accurate identification of unknown pathogens is critical for medicine and public health, yet current metagenomic workflows remain heavily dependent on...
Chest radiography (CXR) is a primary modality for assessing cardiopulmonary conditions, but its effectiveness is limited by anatomical obstructions (e...
The recurrence rate following complete surgical resection of primary non-small cell lung cancer is as high as 55%, yet no approach currently exists to...
Radiogenomics enables the non invasive characterisation of the genomic and molecular properties of tumours, with epidermal growth factor receptor (EGF...