Pulmonology

Temporal Knowledge Discovery in Drug-Resistant Tuberculosis: A Decade-Long Machine Learning Analysis From Egyptian Clinical Data.

BACKGROUND: Advanced predictive tools are required due to the worldwide persistence of tuberculosis (TB) and the growing threat of multidrug-resistant tuberculosis (MDR-TB). The rate and precision of traditional diagnostic techniques frequently experience delays. This study uses machine learning (ML) to identify clinical and longitudinal treatment-history risk factors for drug-resistant tuberculos...

Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.

Lung cancer (LC) is one of the leading causes of death globally. Early detection is essential for saving lives and ensuring effective treatment for patients. When medical professionals can proactively diagnose and classify the condition, they can provide safer and more targeted interventions. The development of automated tools for early detection is vital to identify malignant states at their begi...

Gut microbial extracellular vesicles modulate the development of metabolic dysfunction-associated steatohepatitis through the gut-liver axis.

Metabolic dysfunction-associated steatohepatitis (MASH) represents a growing global health challenge due to its propensity to progress to irreversible...

Exploratory prediction model for deep vein thrombosis in intensive care patients after anticoagulation therapy: An observational study.

BACKGROUND: Despite low-molecular-weight heparin (LMWH) prophylaxis, the incidence of deep vein thrombosis (DVT) remains high in intensive care unit (...

Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.

UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise...

Multimodal AI-driven analysis in breast and lung cancer: insights from the OPTIMA prototyping workshop.

This paper reports on insights from the OPTIMA (Optimal Treatment for Patients with Solid Tumours in Europe Through Artificial Intelligence) prototypi...

Utilizing natural language processing (NLP) to identify breast cancer-associated lung metastases from pathology reports to delineate characteristics of this site of recurrence.

PURPOSE: Natural language processing (NLP, artificial intelligence) can enable automated identification of records in large datasets. The purpose of t...

Paradigm shift in medicine and in clinical reasoning method: time has come for a new chest semiotics.

INTRODUCTION: Clinical reasoning in medicine is a complex cognitive process that integrates sensory perception, interpretation, and abductive inferenc...

Enhancing lung diseases recognition through CNN-RNN methodologies.

Diagnostics of respiratory disorders greatly benefit from medical imaging, especially X-ray imaging, which offers important information about the anat...

Automated Detection and Classification of Pleural Effusion on Computed Tomography Using Deep Learning.

The aim of this study is to develop and evaluate the performance of a two-stage deep learning-based artificial intelligence framework for the automati...

The Role of Machine Learning and Artificial Intelligence in Drug Discovery and Clinical Care of Pulmonary Hypertension.

Pulmonary hypertension (PH) is a severe and oftentimes fatal disease with a high degree of clinical variability. Its complexity necessitates a multifa...

Parsimonious and explainable biomarker-based severity score for hospitalised patients with COVID-19-related respiratory infections: development, validation and XAI benchmarking.

BACKGROUND: Severity scoring systems are increasingly important tools for stratifying hospitalised patients, guiding treatment decisions, and enabling...

LCPBert: ProtBERT-based early-stage lung cancer prediction from T cell receptor beta sequences.

Early detection of lung cancer remains challenging due to limitations of current methods. We developed LCPBert, a deep learning framework leveraging p...

Detection of Interstitial Lung Abnormalities on Chest Radiographs: Diagnostic Performance of Radiologists and Artificial Intelligence.

Background: Interstitial lung abnormalities (ILA) on chest CT are receiving growing attention given their association with progression to interstitial...

Accelerated Aging and Aging Velocity from Deep Learning-based Chest Radiograph-derived Age for Predicting Cause-specific Mortality.

Purpose To assess the prognostic value of deep learning-derived radiographic age and aging velocity for predicting mortality in an Asian cohort. Mater...

Rapid screening of potent and mechanistically insightful repurposable anticancer drugs targeting EGFR for non-small cell lung cancer: machine learning-aided and structure-guided approach.

Despite the initial success of EGFR-targeted therapies in non-small cell lung cancer (NSCLC), the emergence of drug resistance remains a significant c...

Echocardiographic Evaluation of Global Cardiac Function in Systemic Sclerosis-Associated Pulmonary Arterial Hypertension: An Integrated Review of Parameters and Techniques.

Systemic sclerosis (SSc) is a connective tissue disease frequently complicated by pulmonary arterial hypertension (PAH), a leading cause of morbidity ...

A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.

Deep-Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic Interstitial Lung Disease.

BACKGROUND AND OBJECTIVE: High resolution computed tomography (HRCT) scan diagnostic classification for usual interstitial pneumonia (UIP) plays a cri...

Evaluation of AI-Based Medical Device Concerning Localization Information Using Nonparametric Inference for the Alternative Free-Response ROC Curve.

The alternative free-response receiver operating characteristic (AFROC) curve is a popular method for evaluating the performance of diagnostic tests c...