Pulmonology

Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.

The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volu...

A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign.

OBJECTIVES: With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the...

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has b...

Deep learning-based lung image registration: A review.

Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical application...

Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios.

A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI s...

Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study.

Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag pa...

Classification of Pulmonary Nodules in 2-[F]FDG PET/CT Images with a 3D Convolutional Neural Network.

PURPOSE: 2-[F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn feat...

Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT.

PURPOSE: To investigate the performance of deep learning and radiomics features of intra-tumoral region (ITR) and peri-tumoral region (PTR) in the dia...

Fully Automatic Dual-Probe Lung Ultrasound Scanning Robot for Screening Triage.

Two-dimensional lung ultrasound (LUS) has widely emerged as a rapid and noninvasive imaging tool for the detection and diagnosis of coronavirus diseas...

Deep learning algorithm for visual quality assessment of the spirograms.

. The quality of spirometry manoeuvres is crucial for correctly interpreting the values of spirometry parameters. A fundamental guideline for proper q...

Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis.

Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patte...

A machine learning classifier using 33 host immune response mRNAs accurately distinguishes viral and non-viral acute respiratory illnesses in nasal swab samples.

BACKGROUND: Viral acute respiratory illnesses (viral ARIs) contribute significantly to human morbidity and mortality worldwide, but their successful t...

Mouth-Opener Technique for Ultrasound-Guided Inferior Alveolar Nerve Block: A Case Series of Extraction of Impacted Mandibular Third Molars.

Extraction of the impacted mandibular third molar (IMTM) is common in oral surgery, but its postoperative pain is severe. Ultrasound-guided inferior a...

Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships.

Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to a...

Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series.

BACKGROUND: Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberc...

Comprehensive study of algal blooms variation in Jiaozhou Bay based on google earth engine and deep learning.

The Jiaozhou Bay ecosystem, a crucial marine ecosystem in China, has been plagued by frequent harmful algal blooms as due to deteriorating water quali...

Application of Deep Learning Techniques for Detection of Pneumothorax in Chest Radiographs.

With the advent of Artificial Intelligence (AI) and even more so recently in the field of Machine Learning (ML), there has been rapid progress across ...

Artificial Intelligence Improves Novices' Bronchoscopy Performance: A Randomized Controlled Trial in a Simulated Setting.

BACKGROUND: Navigating through the bronchial tree and visualizing all bronchial segments is the initial step toward learning flexible bronchoscopy. A ...

Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters.

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitor...

An overview of ultrasound-derived radiomics and deep learning in liver.

Over the past few years, developments in artificial intelligence (AI), especially in radiomics and deep learning, have enabled the extraction of patho...