AIMC Topic: Lung Diseases

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A Physics-Informed Deep Neural Network for Harmonization of CT Images.

IEEE transactions on bio-medical engineering Nov 21, 2024
OBJECTIVE: Computed Tomography (CT) quantification is affected by the variability in image acquisition and rendition. This paper aimed to reduce this variability by harmonizing the images utilizing physics-based deep neural networks (DNNs).
Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Lung Diseases Phantoms, Imaging Algorithms Tomography, X-Ray Computed Lung Deep Learning Humans Neural Networks, Computer
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Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.

BMC pediatrics Nov 21, 2024
BACKGROUND: Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model...
Humans Machine Learning Retrospective Studies Infant, Extremely Premature Nomograms Lung Diseases Hemorrhage Female Infant, Newborn Male Respiratory Distress Syndrome, Newborn
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Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study.

European journal of radiology Oct 10, 2024
Chest X-rays (CXRs) are crucial for diagnosing and managing lung conditions. While CXR is a common and cost-effective diagnostic tool, interpreting the high volume of CXRs is challenging due to workforce limitations. Artificial intelligence (AI) offe...
Aged Aged, 80 and over Young Adult Adult Prospective Studies Female Reproducibility of Results Adolescent Triage Lung Diseases Artificial Intelligence Radiography, Thoracic Software Sensitivity and Specificity Humans Male Middle Aged
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Deep-learning model accurately classifies multi-label lung ultrasound findings, enhancing diagnostic accuracy and inter-reader agreement.

Scientific reports Sep 27, 2024
Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operators' competence constrains its effectiveness. We developed a deep-learning (DL) model for multi-label classification using LUS and validated its perfo...
Female Aged Humans Retrospective Studies Observer Variation Lung Deep Learning Adult Ultrasonography Male Lung Diseases Middle Aged
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Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy.

Anaesthesia, critical care & pain medicine Sep 13, 2024
BACKGROUND: Postoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a machine learning-based prediction model was developed to identify patients at high risk of developi...
Enhanced Recovery After Surgery Laparoscopy Humans Lung Diseases Machine Learning Hepatectomy Male Aged Middle Aged Adult Postoperative Complications Retrospective Studies Female
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Fuzzy lattices assisted EJAYA Q-learning for automated pulmonary diseases classification.

Biomedical physics & engineering express Sep 3, 2024
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices forma...
Tuberculosis Fuzzy Logic Pneumonia Lung Diseases Humans Machine Learning Sensitivity and Specificity Image Processing, Computer-Assisted Algorithms Reproducibility of Results
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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...
Severity of Illness Index Bayes Theorem Lung Diseases Radiography, Thoracic SARS-CoV-2 Male Middle Aged Monte Carlo Method Humans Neural Networks, Computer COVID-19 Female
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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...
Diagnosis, Differential Lung Diseases Hamartoma Tomography, X-Ray Computed Deep Learning Female ROC Curve Radiomics Humans Aged Lung Neoplasms Male Retrospective Studies Adult Middle Aged
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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
Monitoring, Intraoperative Humans Lung Diseases Machine Learning Postoperative Complications
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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...
Artificial Intelligence Biomarkers Lung Diseases Humans
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