Pulmonology

Monitoring of Lung Ultrasound Acquisition using Infrared Sensors and Artificial Intelligence.

Lung diseases contribute significantly to global mortality rates. The conventional diagnostic techniques based on medical imaging used for lung disease diagnosis normally require specialized personnel and complex infrastructure, posing challenges in rural areas. In response to these challenges, Volume Sweep Imaging in the lung (VSI-L) is an ultrasound-based acquisition protocol designed to empower...

Machine Learning Model Combining Ventilatory, Hypoxic, Arousal Domains Across Sleep Better Predicts Adverse Consequences of Obstructive Sleep Apnea.

Obstructive sleep apnea(OSA) severity is currently assessed clinically using the apnea-hypopnea index (AHI), which is inconsistently associated with short- and long-term outcomes. Ventilatory, hypoxic, and arousal domains are known to exhibit abnormalities in OSA. Using the same set of features across these three domains, albeit using different models, we show that a physiology-guided ML approach ...

Efficient Lung Segmentation from Chest Radiographs using Transfer Learning and Lightweight Deep Architecture.

Lung delineation constitutes a critical preprocessing stage for X-ray-based diagnosis and follow-up. However, automatic lung segmentation from chest r...

Automatic COVID-19 Detection from Chest X-ray using Deep MobileNet Convolutional Neural Network.

As the COVID-19 pandemic has put a strain on healthcare systems around the world, accurate and rapid virus detection has become increasingly important...

Predicting Hemodynamic and Pulmonary Decompensation with Deep Neural Networks: Performance and Explainability.

Predicting the deterioration of patients' hemodynamic and pulmonary decompensation state while being treated on an intensive care unit is demanding fo...

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants.

In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment wi...

KAMLN: A Knowledge-aware Multi-label Network for Lung Cancer Complication Prediction.

Surgical resection is now the only curative approach for early stage lung cancer patients. However, postoperative complications pose a significant thr...

Diagnosis of Pneumoconiosis with Machine Learning.

Pneumoconiosis encompasses a group of lung diseases caused by inhaling dust particles. Frequently recognized as an occupational disease, it primarily ...

Cough-DL: A Deep Learning Model for Ear-Worn Cough Detection.

Cough serves as a crucial bio-marker for evaluation and monitoring of pulmonary conditions. With growing interest towards automatic cough detection sy...

STConvSleepNet: A Spatiotemporal Convolutional Network for Sleep Posture Detection.

Sleep posture, intricately connected to sleep health, has emerged as a crucial focus in sleep medicine. Studies have associated the supine posture wit...

Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data.

Medical image analysis has witnessed a paradigm shift with the advent of artificial intelligence, particularly the application of Vision Transformers ...

Advancing Chest X-ray Diagnostics via Multi-Modal Neural Networks with Attention.

The healthcare field is undergoing a profound shift, with deep learning in AI increasingly augmenting medical expertise in complex and challenging tas...

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 mi...

Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual medi...

[Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models].

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the...

Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking

Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-e...

Regulating AI Adaptation: An Analysis of AI Medical Device Updates

While the pace of development of AI has rapidly progressed in recent years, the implementation of safe and effective regulatory frameworks has lagge...

Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion

Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists wit...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) complicated by lung infections using interpretable ma...

Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks

Tuberculosis persists as a global health crisis, especially in resource-limited populations and remote regions, with more than 10 million individual...