Pulmonology

Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study.

RATIONALE: Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretati...

[Port-only 4-Arms Robotic Segmentectomy Under Artificial Pneumothorax].

BACKGROUND: At present, robotic surgery is widely used in thoracic surgery, which has higher maneuverability, precision, and stability, especially for small space complex operations and reconstructive surgery. The advantages of robotic lung segment resection under full orifice artificial pneumothorax are obvious.

A framework of deep learning networks provides expert-level accuracy for the detection and prognostication of pulmonary arterial hypertension.

AIMS: To test the hypothesis that deep learning (DL) networks reliably detect pulmonary arterial hypertension (PAH) and provide prognostic information...

Using deep learning to diagnose pulmonary hypertension.

End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model.

BACKGROUND: The extent of interstitial fibrosis in the kidney not only correlates with renal function at the time of biopsy but also predicts future r...

Deep Learning-based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-Resolution Computed Tomography.

Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging...

ANESTHESIA FOR ROBOT-ASSISTED RADICAL PROSTATECTOMY - A CHALLENGE FOR ANAESTHESIOLOGIST.

Mininimally invasive surgery has become one of the most popular ones over the last few decades due to many benefits. The advantages are minimal surgic...

Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.

BACKGROUND: Artificial intelligence (AI) and deep learning have shown great potential in streamlining clinical tasks. However, most studies remain con...

Artificial intelligence in lung cancer: Application and future thinking.

The availability of medical big data and the rapid development of computer software and hardware have greatly promoted the advancement of intelligent ...

CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19.

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease ...

[Lung parenchyma segmentation based on double scale parallel attention network].

[]Automatic and accurate segmentation of lung parenchyma is essential for assisted diagnosis of lung cancer. In recent years, researchers in the field...

[Comparison of the predictive performance of Logistic regression, BP neural network and support vector machine model for the risk of acute exacerbation of readmission in elderly patients with chronic obstructive pulmonary disease within 30 days].

OBJECTIVE: To compare the effectiveness of Logistic regression, BP neural network and support vector machine models in the prediction of 30-day risk o...

Deep Learning Empowers Lung Cancer Screening Based on Mobile Low-Dose Computed Tomography in Resource-Constrained Sites.

BACKGROUND: Existing challenges of lung cancer screening included non-accessibility of computed tomography (CT) scanners and inter-reader variability,...

Fuzzy-Based Expert Supervision System for Feedback Controlled Oxygenation.

Supervision of mechanical ventilation is currently still performed by clinical staff. With the increasing level of automation in the intensive care un...

Utilizing Deep Learning on Limited Mobile Speech Recordings for Detection of Obstructive Pulmonary Disease.

Passive assessment of obstructive pulmonary disease has gained substantial interest over the past few years in the mobile and wearable computing commu...

Feasibility of a Deep Learning approach to estimate Shear Wave Speed using the framework of Reverberant Shear Wave Elastography: A numerical simulation study.

Reverberant Shear Wave Elastography (RSWE) is an ultrasound elastography technique that offers great advantages, however, current estimators generate ...

Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound.

Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung path...

CapNet: A Deep Learning-based Framework for Estimation of Capnograph Signal from PPG.

Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnogra...

An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies.

This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from input audio. Audio recordings of respiratory cycles ...

Substantive Interpretation of Machine Learning Solutions by the Example of Determining the Activity of the Tuberculosis Process in Individuals with Minimal Tuberculosis Residual Changes.

In this article is described an application of various machine learning (ML) methods to obtain decision rules and its interpretation to a problem of r...