AIMC Topic: Lung
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Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis.
Sensors (Basel, Switzerland)
Feb 1, 2024
The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms of diagnosis and management of thoracic disorders. This study proposes a comprehensive fra...
Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Jan 31, 2024
This study aimed to investigate whether deep-learning reconstruction (DLR) improves interobserver agreement in the evaluation of honeycombing for patients with interstitial lung disease (ILD) who underwent high-resolution computed tomography (CT) co...
A three-gene random forest model for diagnosing idiopathic pulmonary fibrosis based on circadian rhythm-related genes in lung tissue.
Expert review of respiratory medicine
Jan 31, 2024
BACKGROUND: The disorder of circadian rhythm could be a key factor mediating fibrotic lung disease Therefore, our study aims to determine the diagnostic value of circadian rhythm-related genes (CRRGs) in IPF.
Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound.
Ultrasonics
Jan 29, 2024
Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator v...
Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks.
PloS one
Jan 26, 2024
For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific fe...
Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.
Physical and engineering sciences in medicine
Jan 10, 2024
MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study aims to evaluate how deep-learning (DL) models for target tracking that are trained on data from on...
Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis.
Computer methods and programs in biomedicine
Jan 4, 2024
OBJECTION: The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features.
Specneuzhenide improves bleomycin-induced pulmonary fibrosis in mice via AMPK-dependent reduction of PD-L1.
Phytomedicine : international journal of phytotherapy and phytopharmacology
Dec 30, 2023
BACKGROUND: Pulmonary fibrosis (PF) is an escalating global health issue, characterized by rising rates of morbidity and mortality annually. Consequently, further investigation of potential damage mechanisms and potential preventive strategies for PF...
Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT.
European radiology
Dec 27, 2023
OBJECTIVES: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.
CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.
The international journal of cardiovascular imaging
Dec 27, 2023
The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagn...
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