Pulmonology

Shape-Sensing Robotic-Assisted Bronchoscopy in the Diagnosis of Pulmonary Parenchymal Lesions.

BACKGROUND: The landscape of guided bronchoscopy for the sampling of pulmonary parenchymal lesions is evolving rapidly. Shape-sensing robotic-assisted bronchoscopy (ssRAB) recently was introduced as means to allow successful sampling of traditionally challenging lesions.

Optimal number of strong labels for curriculum learning with convolutional neural network to classify pulmonary abnormalities in chest radiographs.

BACKGROUND AND OBJECTIVE: It is important to alleviate annotation efforts and costs by efficiently training on medical images. We performed a stress test on several strong labels for curriculum learning with a convolutional neural network to differentiate normal and five types of pulmonary abnormalities in chest radiograph images.

Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.

To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional ...

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.

The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable ...

Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning.

We are developing imaging methods for a co-clinical trial investigating synergy between immunotherapy and radiotherapy. We perform longitudinal micro-...

A Soft Robot for Peripheral Lung Cancer Diagnosis and Therapy.

Lung cancer is one of the deadliest forms of cancers and is often diagnosed by performing biopsies with the use of a bronchoscope. However, this diagn...

An approach to the classification of COVID-19 based on CT scans using convolutional features and genetic algorithms.

COVID-19 is a respiratory disease that, as of July 15th, 2021, has infected more than 187 million people worldwide and is responsible for more than 4 ...

Resolution-based distillation for efficient histology image classification.

Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive s...

Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system.

Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret...

Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism.

PURPOSE:  Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate t...

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and...

Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.

OBJECTIVE: A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherap...

Lung nodule detection in chest X-rays using synthetic ground-truth data comparing CNN-based diagnosis to human performance.

We present a method to generate synthetic thorax radiographs with realistic nodules from CT scans, and a perfect ground truth knowledge. We evaluated ...

Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.

Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible t...

Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs.

Background Determining the activity of pulmonary tuberculosis on chest radiographs is difficult. Purpose To develop a deep learning model to identify ...

Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model.

SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not acc...

Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach.

BACKGROUND: Artificial intelligence approaches can integrate complex features and can be used to predict a patient's risk of developing lung cancer, t...

RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.

The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed to...

Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step...

Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.

Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning, monitoring, and image-guided interventions of lung cancer ...