Pulmonology

[Prediction of recurrence-free survival in lung adenocarcinoma based on self-supervised pre-training and multi-task learning].

Computed tomography (CT) imaging is a vital tool for the diagnosis and assessment of lung adenocarcinoma, and using CT images to predict the recurrence-free survival (RFS) of lung adenocarcinoma patients post-surgery is of paramount importance in tailoring postoperative treatment plans. Addressing the challenging task of accurate RFS prediction using CT images, this paper introduces an innovative ...

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.

BACKGROUND: The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer scree...

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but...

Radiomics and Deep Learning to Predict Pulmonary Nodule Metastasis at CT.

Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.

BACKGROUND: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic ...

B-mode US and Deep Learning Rivals Shear-Wave Elastography in Screening for Fibrosis.

[Analysis of clinical treatment of acute respiratory distress syndrome assisted by artificial intelligence].

OBJECTIVE: To evaluate the clinical practice of intensive care unit (ICU) physicians at Hebei General Hospital in identifying patients meeting the dia...

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug se...

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance].

OBJECTIVE: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of pati...

Development of deep-learning models for real-time anaerobic threshold and peak VO2 prediction during cardiopulmonary exercise testing.

AIMS: Exercise intolerance is a clinical feature of patients with heart failure (HF). Cardiopulmonary exercise testing (CPET) is the first-line examin...

Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality.

OBJECTIVES: The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep l...

AI for Detection of Tuberculosis: Implications for Global Health.

Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiograph...

Minimally invasive surgery for clinical T4 non-small-cell lung cancer: national trends and outcomes.

OBJECTIVES: Recent randomized data support the perioperative benefits of minimally invasive surgery (MIS) for non-small-cell lung cancer (NSCLC). Its ...

Artificial Intelligence in Pediatric Respiratory Diseases: Current Status and Future Promises.

Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern.

Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibros...

Quantifying Fibrosis in Fibrotic Lung Disease: A Good Human Plus a Machine Is the Best Combination?

Man versus Machine in Pleural Diagnostics: Does Artificial Intelligence Provide the Solution?

Motivation and Learning: Leveraging Artificial Intelligence to Improve Bronchoscopy Performance.

Artificial Intelligence Feedback for Bronchoscopy Training: Old Wine in a New Bottle or True Innovation?

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction.

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative predi...