Pulmonology

Latest AI and machine learning research in pulmonology for healthcare professionals.

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CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

PURPOSE: Patients with pneumonia often present to the emergency department (ED) and require prompt d...

Efficient and targeted COVID-19 border testing via reinforcement learning.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of a...

The smell of lung disease: a review of the current status of electronic nose technology.

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhale...

Genetic dissection of complex traits using hierarchical biological knowledge.

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to ac...

Cefepime Induced Neurotoxicity Following A Regimen Dose-Adjusted for Renal Function: Case Report and Review of the Literature.

Cefepime induced neurotoxicity (CIN) is commonly associated with renal dysfunction, however CIN can...

FluNet: An AI-Enabled Influenza-Like Warning System.

Influenza is an acute viral respiratory disease that is currently causing severe financial and resou...

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from ...

Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning.

Coughing is a common symptom for many respiratory disorders, and can spread droplets of various size...

New Technique for Introducing a Surgical Stapler during Robot-Assisted Lobectomy for Lung Cancer.

BACKGROUND: The da Vinci Si version robot lacks a vascular stapler that can be controlled by the ope...

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.

PURPOSE: The identification of pathological mediastinal lymph nodes is an important step in the stag...

Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning.

Intraoperative detection of the marginal tissues is the last and most important step to complete the...

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early iden...

A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network.

Maximum intensity projection (MIP) technology is a computer visualization method that projects three...

Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning.

PURPOSE: The deformable registration of 3D chest computed tomography (CT) images is one of the most ...

A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM.

Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical ...

Technical details for a robot-assisted hand-sewn esophago-gastric anastomosis during minimally invasive Ivor Lewis esophagectomy.

BACKGROUND: Minimally invasive Ivor Lewis esophagectomy (MIILE) provides better outcomes than open t...

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

BACKGROUND AND OBJECTIVE: Given that the novel coronavirus disease 2019 (COVID-19) has become a pand...

Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks.

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biolo...

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.

Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies du...

Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

Although deep learning networks applied to digital images have shown impressive results for many pat...

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