Pulmonology

Label-free Pathogen Identification with Microscopy Imaging and Deep Learning

Rapid and accurate pathogen identification is crucial for the clinical management of infectious diseases, particularly sepsis and severe respiratory infections, yet standard clinical workflows remain slow and resource-intensive. Here, we developed an automated, high-throughput imaging platform built on standard, clinically accessible bright-field microscopy, and generated a large dataset comprisin...

Deep learning-based detection of cessation of breathing in pre-term infants

Apnoea of prematurity is characterised by recurrent episodes of cessation of breathing and remains difficult to detect reliably using routinely monitored physiological signals in the Neonatal Intensive Care Unit (NICU). Existing bedside monitors rely primarily on respiratory rate and oxygen saturation thresholds, often generating high false-positive alarm rates and missing short or irregular event...

Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio

Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We p...

Multi-cancer detection using a computationally efficient CNN with transfer learning

This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer det...

Human and AI collaboration for pulmonary nodule segmentation

Medical expert annotators are scarce, and blind reliance on artificial intelligence (AI) can be misleading, motivating approaches in which humans, par...

Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives

Information extraction from pathology reports is essential for cancer staging, tumor registry population. Yet key data remains embedded in narrative r...

Beyond AHI: An Interpretable Causal-Discovery-Guided Framework for Sleep Recovery in Connected Health

Objective sleep assessment relies on polysomnography (PSG), yet clinical impact is often better reflected in patient-reported outcomes (PROs) such as ...

Usability testing with a prototype user interface of an Artificial Intelligence driven air-Safety Tool (AISaT)

Involving end-users in the development of an AI tool is an important facilitator to its implementation. Usability testing was therefore conducted with...

Artificial Intelligence-Based Detection of Airway Mucus Plugs on CT and Associations With Clinical Outcomes in COPDGene

RATIONALE: Airway mucus plugging is a clinically relevant manifestation of airway pathology in chronic obstructive pulmonary disease (COPD) and is ass...

Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation

Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substa...

Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images

Four-dimensional computed tomography (4DCT) captures the full respiratory cycle of thoracic anatomy, yet current Internal Target Volume contouring wor...

Trusting Right Predictions for Wrong Reasons: A LIME Based Analysis of Deep Learning Interpretability in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related mortality, with approximately 2.5 million new cases and 1.8 million deaths annually, making reliabl...

OmicsNavigator: An auditable scientific partner for scalable hypothesis validation in spatial omics

Translating high-dimensional, spatially resolved molecular datasets into testable biological findings remains a major research bottleneck. Here, we pr...

A Machine Learning Pipeline for Scalable Annotation of Patient-Ventilator Dyssynchrony from Bedside Ventilator Data

Objective: Patient-ventilator dyssynchrony (PVD) is a common and clinically consequential problem in critically ill patients receiving invasive mechan...

Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata

AI governance for medical imaging is formalizing: the 2026 ACR-SIIM Practice Parameter recommends local acceptance testing and ongoing drift monitorin...

Exploratory Assessment of Pulsed-Wave Doppler Representations of Lung Sounds Using Deep Learning: An In-Vitro Phantom Study

The increasing availability of portable ultrasound systems motivates exploration of novel approaches to respiratory signal assessment. In this in-vitr...

Optimisation of steatotic liver disease screening algorithm for resource-poor settings using machine learning

Background The European Association for the Study of the Liver (ESAL) - Steatotic Liver Disease (SLD) screening algorithm involves two steps; initial ...

Assessment of the accuracy of lung lesions diagnosis in adolescents with osteosarcoma using artificial intelligence

Background. Lung metastases in osteosarcoma (OS) are the main cause of the death. The accuracy of the diagnosis of nodules by computed tomography (CT)...

Development and Prospective Validation of Predictive Model for Early Hemodynamic Deterioration in Critical Care: A Multicenter Study

High-frequency physiological monitoring in ICUs can identify impending deterioration hours before clinical recognition yet extracting reliable early-w...

Time-Conditioned and Multi-Time Survival Prediction from 2D PET/CT Projections in Lung Cancer

Accurate prediction of overall survival (OS) from positron emission tomography/computed tomography (PET/CT) can support personalized treatment and fol...