Pulmonology

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma.

Oral cancer is a global health challenge with a difficult histopathological diagnosis. The accurate histopathological interpretation of oral cancer tissue samples remains difficult. However, early diagnosis is very challenging due to a lack of experienced pathologists and inter- observer variability in diagnosis. The application of artificial intelligence (deep learning algorithms) for oral cancer...

Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.

Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operators' competence constrains its effectiveness. We developed a deep-learning (DL) model for multi-label classification using LUS and validated its performance and efficacy on inter-reader variability. We retrospectively collected LUS and labeled as normal, B-line, consolidation, and effusion from pati...

Length-scale study in deep learning prediction for non-small cell lung cancer brain metastasis.

Deep learning-assisted digital pathology has demonstrated the potential to profoundly impact clinical practice, even surpassing human pathologists in ...

Development and validation of machine learning models for diagnosis and prognosis of lung adenocarcinoma, and immune infiltration analysis.

The aim of our study was to develop robust diagnostic and prognostic models for lung adenocarcinoma (LUAD) using machine learning (ML) techniques, foc...

Estimating intra- and inter-subject oxygen consumption in outdoor human gait using multiple neural network approaches.

Oxygen consumption ([Formula: see text]) is an important measure for exercise test, such as walking and running, that can be measured outdoors using p...

Predictive models for heat stress assessment in Holstein dairy heifers using infrared thermography and machine learning.

Heat stress is a condition that impairs the animal's productive and reproductive performance, and can be monitored by physiological and environmental ...

Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study.

BACKGROUND: The optimal number of lymph nodes to be dissected during lung cancer surgery to minimise the postoperative recurrence risk remains undeter...

Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due t...

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the...

Evaluating ChatGPT as a patient resource for frequently asked questions about lung cancer surgery-a pilot study.

OBJECTIVE: Chat-based artificial intelligence programs like ChatGPT are reimagining how patients seek information. This study aims to evaluate the qua...

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis a...

Screening COPD-Related Biomarkers and Traditional Chinese Medicine Prediction Based on Bioinformatics and Machine Learning.

PURPOSE: To employ bioinformatics and machine learning to predict the characteristics of immune cells and genes associated with the inflammatory respo...

Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records.

Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify ...

Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage.

AIMS: To investigate radiology staff perceptions of an AI tool for chest radiography triage, flagging findings suspicious for lung cancer to expedite ...

Identification of Nocturnal Leg Cramps and Affecting Factors in COPD Patients: Logistic Regression and Artificial Neural Network.

Although there are many sleep-related complaints in chronic obstructive pulmonary disease (COPD) patients, nocturnal leg cramps have not been adequate...

GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.

PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and ...

Radiograph accelerated detection and identification of cancer in the lung (RADICAL): a mixed methods study to assess the clinical effectiveness and acceptability of Qure.ai artificial intelligence software to prioritise chest X-ray (CXR) interpretation.

INTRODUCTION: Diagnosing and treating lung cancer in early stages is essential for survival outcomes. The chest X-ray (CXR) remains the primary screen...

Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study.

OBJECTIVE: Develop a predictive model utilizing weakly supervised deep learning techniques to accurately forecast major pathological response (MPR) in...

Radiomics-Based Support Vector Machine Distinguishes Molecular Events Driving the Progression of Lung Adenocarcinoma.

INTRODUCTION: An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD ...

Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules.

INTRODUCTION: Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately dete...