AIMC Topic: Lung Diseases
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Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.
Journal of imaging informatics in medicine
Aug 20, 2024
In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1...
Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas: a retrospective cohort study.
International journal of surgery (London, England)
Aug 1, 2024
BACKGROUND: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical featu...
Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications. Comment on Br J Anaesth 2024; 132: 1315-26.
British journal of anaesthesia
Jul 15, 2024
Exploring explainable AI features in the vocal biomarkers of lung disease.
Computers in biology and medicine
Jul 8, 2024
This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health ch...
Optimization of vision transformer-based detection of lung diseases from chest X-ray images.
BMC medical informatics and decision making
Jul 8, 2024
BACKGROUND: Recent advances in Vision Transformer (ViT)-based deep learning have significantly improved the accuracy of lung disease prediction from chest X-ray images. However, limited research exists on comparing the effectiveness of different opti...
Computer-aided diagnosis of cystic lung diseases using CT scans and deep learning.
Medical physics
Jun 22, 2024
BACKGROUND: Auxiliary diagnosis of different types of cystic lung diseases (CLDs) is important in the clinic and is instrumental in facilitating early and specific treatments. Current clinical methods heavily depend on accumulated experience, restric...
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed ...
Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.
IEEE journal of translational engineering in health and medicine
May 9, 2024
OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteri...
DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.
Sensors (Basel, Switzerland)
Apr 29, 2024
Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results i...
Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.
British journal of anaesthesia
Apr 18, 2024
BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that ...
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