Pulmonology

Development of Computational Pipeline for Right Ventricular Hemodynamic Single-Beat Analysis

Load-independent indices of right ventricular (RV) dysfunction aid in the prognosis of patients with pulmonary hypertension (PH), but their acquisition remains difficult. This study aimed to develop a novel computer vision artificial intelligence-based pipeline that can estimate load-independent RV functional indices using screenshots of the RV pressure-time waveform from a standard clinical right...

Phenotypic and prognostic insights through unbiased self-supervised learning on kidney histology

Deep learning methods for image segmentation and classification in histopathology generally utilize supervised learning, relying on manually created labels for model development. Here, we applied a self-supervised framework to characterize kidney histology without the use of pathologist annotations, training on whole slide images to identify histomorphological phenotype clusters (HPCs) and create ...

Urinary collagen peptides predict mortality

Organ fibrosis caused by the presence of excessive extracellular matrix (ECM) is strongly related to mortality. Urinary peptide signatures were report...

Exploring Novel Biomarkers for Early Detection of Osteoporosis

Osteoporosis is characterized by diminished BMD and deteriorated bone microstructure, significantly increasing fracture susceptibility. This study lev...

Towards automated multi-regional lung parcellation for 0.55-3T 3D T2w fetal MRI

Fetal MRI is increasingly being employed in the diagnosis of fetal lung anomalies and segmentation-derived total fetal lung volumes are used as one of...

Machine Learning Analysis of Electronic Health Records Identifies Interstitial Lung Disease and Predicts Mortality in Patients with Systemic Sclerosis

Interstitial lung disease (ILD) is the leading cause of death in patients with systemic sclerosis (SSc), affecting more than 40% of this population. D...

A Deep Learning Lung Cancer Segmentation Pipeline to Facilitate CT-based Radiomics

CT-based radio-biomarkers could provide non-invasive insights into tumour biology to risk-stratify patients. One of the limitations is laborious manua...

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

Pediatric sarcomas are rare and diverse, often leading to misclassification that hampers prognosis and treatment planning. We collected and harmonized...

Data-driven discovery of core sleep biomarkers for predicting early cardiometabolic risk in a healthy population using machine learning

Identifying robust biomarkers for future cardiometabolic risk within the crucial “ preventive window” in healthy individuals remains a major challenge...

Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions

Molecular and cellular alterations to the normal pseudostratified columnar bronchial epithelium results in the development of bronchial premalignant l...

A Hybrid AutoML Ensemble Integrating Conventional Learners and Gradient-Boosting Models for Multi-Outcome Prediction in ICU Patients with Pseudomonas aeruginosa

Carbapenem resistance in Pseudomonas aeruginosa is increasing in intensive care units (ICUs). To enhance antimicrobial stewardship and infection contr...

Leveraging Machine Learning for Developing and Validating a Neonatal Acute Kidney Injury Prediction Model (NEPHRO): A Comprehensive Evidence-Based Neonatal AKI Risk Stratification Tool

Acute kidney injury (AKI) is a serious and common complication among critically ill neonates. Preventing or treating AKI early requires timely predict...

Cardiac Function Assessment with Deep-Learning-Based Automatic Segmentation of Free-Running 4D Whole-Heart CMR

Free-running (FR) cardiac MRI enables free-breathing ECG-free fully dynamic 5D (3D spatial+cardiac+respiration dimensions) imaging but poses significa...

Development of a pilot machine learning model to predict successful cure in critically ill patients with community-acquired pneumonia

Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict sho...

Efficient Chest X-Ray Feature Extraction and Feature Fusion for Pneumonia Detection Using Lightweight Pretrained Deep Learning Models

Pneumonia is a respiratory condition characterized by inflammation of the alveolar sacs in the lungs, which disrupts normal oxygen exchange. This dise...

Evaluating Few-Shot Prompting for Spectrogram-Based Lung Sound Classification Using a Multimodal Language Model

Traditional deep learning models for lung sound analysis require large, labeled datasets; multimodal LLMs may offer a flexible, prompt-based alternati...

DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images

Spatial transcriptomics technology remains resource-intensive and unlikely to be routinely adopted for patient care soon. This hinders the development...

Predicting Cardiopulmonary Exercise Testing Performance in Patients Undergoing Transthoracic Echocardiography - An AI Based, Multimodal Model

Transthoracic echocardiography (TTE) is a widely available tool for diagnosing and managing heart failure but has limited predictive value for surviva...

CLINICAL VALIDATION OF SWAASA ARTIFICIAL INTELLIGENCE PLATFORM USING COUGH SOUNDS FOR SCREENING AND DIAGNOSIS OF RESPIRATORY DISEASES

Analysis of cough sounds have the potential to give a clue regarding the underlying respiratory disease. The Swaasa AI platform using artificial intel...

Comprehensive characterization of granular fibrotic and cellular features in liver tissue enabled by deep learning models

Histologic staging of metabolic dysfunction-associated steatohepatitis (MASH) requires semiquantitative assessment of hepatocellular ballooning, steat...