Latest AI and machine learning research in pulmonology for healthcare professionals.
BACKGROUND: Despite low-molecular-weight heparin (LMWH) prophylaxis, the incidence of deep vein thrombosis (DVT) remains high in intensive care unit (ICU) patients, creating a need for personalised risk assessment to enable precision prevention. OBJECTIVES: The aim of this study was to develop and validate a prediction model for lower extremity DVT in ICU patients already receiving LMWH, thereby i...
UNLABELLED: Pediatric sarcomas present diagnostic challenges due to their rarity and diverse subtypes, often requiring specialized pathology expertise and costly genetic tests. To overcome these barriers, we developed a computational pipeline leveraging deep learning methods to accurately classify pediatric sarcoma subtypes from digitized histology slides. To ensure classifier generalizability and...
This paper reports on insights from the OPTIMA (Optimal Treatment for Patients with Solid Tumours in Europe Through Artificial Intelligence) prototypi...
PURPOSE: Natural language processing (NLP, artificial intelligence) can enable automated identification of records in large datasets. The purpose of t...
INTRODUCTION: Clinical reasoning in medicine is a complex cognitive process that integrates sensory perception, interpretation, and abductive inferenc...
Diagnostics of respiratory disorders greatly benefit from medical imaging, especially X-ray imaging, which offers important information about the anat...
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...
Swine influenza A viruses (swIAV) are a major cause of respiratory disease in pigs, and vaccination remains the main control strategy. The objective o...
Pulmonary hypertension (PH) is a severe and oftentimes fatal disease with a high degree of clinical variability. Its complexity necessitates a multifa...
BACKGROUND: Severity scoring systems are increasingly important tools for stratifying hospitalised patients, guiding treatment decisions, and enabling...
Early detection of lung cancer remains challenging due to limitations of current methods. We developed LCPBert, a deep learning framework leveraging p...
Background: Interstitial lung abnormalities (ILA) on chest CT are receiving growing attention given their association with progression to interstitial...
Purpose To assess the prognostic value of deep learning-derived radiographic age and aging velocity for predicting mortality in an Asian cohort. Mater...
Despite the initial success of EGFR-targeted therapies in non-small cell lung cancer (NSCLC), the emergence of drug resistance remains a significant c...
Systemic sclerosis (SSc) is a connective tissue disease frequently complicated by pulmonary arterial hypertension (PAH), a leading cause of morbidity ...
BACKGROUND AND OBJECTIVE: High resolution computed tomography (HRCT) scan diagnostic classification for usual interstitial pneumonia (UIP) plays a cri...
The alternative free-response receiver operating characteristic (AFROC) curve is a popular method for evaluating the performance of diagnostic tests c...
BACKGROUND: In clinical stage IA lung adenocarcinoma (LUAD), rapid and accurate intraoperative diagnosis is crucial to decide whether to perform segme...
Stereotactic Arrhythmia Radioablation (STAR) is a promising treatment for refractory ventricular tachycardia. However, its precision may be hampered b...