Pulmonology

RespNet: A deep learning model for extraction of respiration from photoplethysmogram.

Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is seldom used outside the intensive care ward due to the large size and cost of the monitoring system. While Electrocardiogram (ECG) based respiration extraction is a validated approach, its adoption is limited by access to ...

Hybrid Neural Networks for Mortality Prediction from LDCT Images.

Known for its high morbidity and mortality rates, lung cancer poses a significant threat to human health and well-being. However, the same population is also at high risk for other deadly diseases, such as cardiovascular disease. Since Low-Dose CT (LDCT) has been shown to significantly improve the lung cancer diagnosis accuracy, it will be very useful for clinical practice to predict the all-cause...

Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure'.

Lung cancer is one of the most fatal cancers in the world. If the lung cancer can be diagnosed at an early stage, the survival rate of patients post t...

Fully Automated Segmentation of Alveolar Bone Using Deep Convolutional Neural Networks from Intraoral Ultrasound Images.

Delineation of alveolar bone aids the diagnosis and treatment of periodontal diseases. In current practice, conventional 2D radiography and 3D cone-be...

Model-free Cardiorespiratory Motion Prediction from X-ray Angiography Sequence with LSTM Network.

We present a novel model-free approach for cardiorespiratory motion prediction from X-ray angiography time series based on Long Short-Term Memory Recu...

[Research on algorithms for identifying the severity of acute respiratory distress syndrome patients based on noninvasive parameters].

Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diag...

[Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT].

BACKGROUND: The detection of pulmonary nodules is a key step to achieving the early diagnosis and therapy of lung cancer. Deep learning based Artifici...

Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach.

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disease that affects people's health, especially in the elderly. In the traditiona...

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study.

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) syste...

Convolutional neural network approach to lung cancer classification integrating protein interaction network and gene expression profiles.

Deep learning technologies are permeating every field from image and speech recognition to computational and systems biology. However, the application...

[New Model for Intelligent Imaging Screening of Pulmonary Nodules].

The artificial intelligence based on medical aid diagnosis has been in full swing in these years. How to better and more safely utilize this new techn...

[Clinical Application of Artificial Intelligence Recognition Technology 
in the Diagnosis of Stage T1 Lung Cancer].

BACKGROUND: Lung cancer is the cancer with the highest morbidity and mortality at home and abroad at present. Using computed tomography (CT) to screen...

Validity of Natural Language Processing for Ascertainment of and Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

PURPOSE: SEER registries do not report results of epidermal growth factor receptor () and anaplastic lymphoma kinase () mutation tests. To facilitate ...

Exploring the survival prognosis of lung adenocarcinoma based on the cancer genome atlas database using artificial neural network.

The aim of this study was to investigate the clinical factors affecting the survival prognosis of lung adenocarcinoma, and to establish a predictive m...

[An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].

Lung cancer is the most common and fatal tumor in the world with limited diagnostic and treatment methods. The development of precision medicine has b...

An intelligent aerator algorithm inspired-by deep learning.

Aerator is an indispensable tool in aquaculture, and China is one of the largest aquaculture countries in the world. So, the intelligent control of th...

Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.

RATIONALE: Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investiga...

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction usi...

Fuzzy logic and hospital admission due to respiratory diseases using estimated values by mathematical model.

Hospitalizations due to respiratory diseases generate financial costs for the Health System in addition to social costs. Objective of this study was t...

RRAM-based synapse devices for neuromorphic systems.

Hardware artificial neural network (ANN) systems with high density synapse array devices can perform massive parallel computing for pattern recognitio...