Pulmonology

A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.

This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung ad...

ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax.

Radiology narrative reports often describe characteristics of a patient's disease, including its loc...

Flow starvation during square-flow assisted ventilation detected by supervised deep learning techniques.

BACKGROUND: Flow starvation is a type of patient-ventilator asynchrony that occurs when gas delivery...

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which refl...

Enhancing pediatric pneumonia diagnosis through masked autoencoders.

Pneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presen...

A review of the current status and progress in difficult airway assessment research.

A difficult airway is a situation in which an anesthesiologist with more than 5 years of experience ...

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contr...

Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.

BACKGROUND AND PURPOSE: To develop a computed tomography (CT)-based deep learning model to predict o...

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.

Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cance...

Binding Activity Classification of Anti-SARS-CoV-2 Molecules using Deep Learning Across Multiple Assays.

BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic, caused by severe acute respiratory syn...

Digital pathology with artificial intelligence analysis provides insight to the efficacy of anti-fibrotic compounds in human 3D MASH model.

Metabolic dysfunction-associated steatohepatitis (MASH) is a severe liver disease characterized by l...

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT.

PURPOSE: Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of death ...

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models.

OBJECTIVE: To quantify the relative importance of demographic, contextual, socio-economic, and nodul...

Deep-learning assisted zwitterionic magnetic immunochromatographic assays for multiplex diagnosis of biomarkers.

Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and...

Machine learning models for the early prediction of ventilator-associated pneumonia: Author's reply.

Intraoperative Features Improve Model Risk Predictions After Coronary Artery Bypass Grafting.

BACKGROUND: Intraoperative physiologic parameters could offer predictive utility in evaluating risk ...

Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm.

This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the...

Prediction of acute methanol poisoning prognosis using machine learning techniques.

Methanol poisoning is a global public health concern, especially prevalent in developing nations. Th...

The effects of robot-assisted laparoscopic surgery with Trendelenburg position on short-term postoperative respiratory diaphragmatic function.

OBJECTIVE: To study how Pneumoperitoneum under Trendelenburg position for robot-assisted laparoscopi...

From CNN to Transformer: A Review of Medical Image Segmentation Models.

Medical image segmentation is an important step in medical image analysis, especially as a crucial p...

Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lu...