Pulmonology

Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm.

BACKGROUND: Tuberculosis (TB) is a disease that mainly affects human lungs. It can be fatal if the treatment is delayed. This study investigates the prediction of treatment failure of TB patients focusing on the features which contributes mostly for drug resistance.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program.

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies...

Predicting cancer using supervised machine learning: Mesothelioma.

BACKGROUND: Pleural Mesothelioma (PM) is an unusual, belligerent tumor that rapidly develops into cancer in the pleura of the lungs. Pleural Mesotheli...

Lung Nodule Detection using Convolutional Neural Networks with Transfer Learning on CT Images.

AIM AND OBJECTIVE: Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for patients with lung cancer. Nu...

Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.

The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis an...

Reply to Wilson: Risk-Stratifying Pulmonary Nodules.

Risk-Stratifying Pulmonary Nodules.

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.

A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques.

Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lower...

Incorporating New Technologies to Overcome the Limitations of Endoscopic Spine Surgery: Navigation, Robotics, and Visualization.

Recently, spine surgery has gradually evolved from conventional open surgery to minimally invasive surgery, and endoscopic spine surgery (ESS) has bec...

Detection of Lung Cancer on Computed Tomography Using Artificial Intelligence Applications Developed by Deep Learning Methods and the Contribution of Deep Learning to the Classification of Lung Carcinoma.

BACKGROUND: Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effectiv...

A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients.

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT ris...

Natural Language Processing Performance for the Identification of Venous Thromboembolism in an Integrated Healthcare System.

Real-time identification of venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) and pulmonary embolism (PE), can inform a healthcare o...

Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study.

BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient...

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinica...

Deep learning assistance for tuberculosis diagnosis with chest radiography in low-resource settings.

Tuberculosis (TB) is a major health issue with high mortality rates worldwide. Recently, tremendous researches of artificial intelligence (AI) have be...

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this...

Tuberculosis detection in chest X-ray using Mayfly-algorithm optimized dual-deep-learning features.

World-Health-Organization (WHO) has listed Tuberculosis (TB) as one among the top 10 reasons for death and an early diagnosis will help to cure the pa...

Analysis of segmentation of lung parenchyma based on deep learning methods.

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared ...

Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients.

The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate...