We propose a shape prior representation-constrained multi-scale features fusion segmentation network for medical image segmentation, including training and testing stages. The novelty of our training framework lies in two modules comprised of the sha...
BACKGROUND: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML a...
The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesio...
PURPOSE OF REVIEW: Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a central role in determining the phenotype of pulmonary hypertension, informing treatment strategies. Man...
In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably grapple wi...
BACKGROUND: Chest x-ray is a basic, cost-effective, and widely available imaging method that is used for static assessments of organic diseases and anatomical abnormalities, but its ability to estimate dynamic measurements such as pulmonary function ...
BACKGROUND: Lung ultrasound can evaluate for pneumothorax but the accuracy of diagnosis depends on experience among physicians. This study aimed to investigate the sensitivity and specificity of intelligent lung ultrasound in comparison with chest x-...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment p...
BACKGROUND: In medical imaging, the integration of deep-learning-based semantic segmentation algorithms with preprocessing techniques can reduce the need for human annotation and advance disease classification. Among established preprocessing techniq...