AIMC Topic: Trachea
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Tracheal Targeted Nanogrid Delivery Systems of Dexamethasone Visualized by Single-Particle Tracing and Multiscale Pathological Mapping.
ACS nano
Jun 4, 2025
Current clinical treatment of pulmonary diseases requires an advanced three-dimensional (3D) pathological atlas of the microenvironment, particularly the trachea, which is predominantly affected by lung disorders. In this study, the gridded cyclodext...
Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.
European radiology
Dec 31, 2024
OBJECTIVE: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.
SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.
Sensors (Basel, Switzerland)
Dec 5, 2024
Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS i...
Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning.
Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Jul 16, 2024
Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screen...
Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method.
Artificial intelligence in medicine
Jul 9, 2024
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...
Impact of rapid iodine contrast agent infusion on tracheal diameter and lung volume in CT pulmonary angiography measured with deep learning-based algorithm.
Japanese journal of radiology
May 11, 2024
PURPOSE: To compare computed tomography (CT) pulmonary angiography and unenhanced CT to determine the effect of rapid iodine contrast agent infusion on tracheal diameter and lung volume.
Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm.
Journal of imaging informatics in medicine
Mar 6, 2024
This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the tracheal diameter and lung volume. In this retrospective study, a total of 221 patients (71.1 ± 12.4 years, 174 males) who underwent vascular dynamic...
Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.
AJR. American journal of roentgenology
Sep 13, 2023
Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. The purpose of our study was to evaluate the performance of a deep learning (DL...
Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation.
Critical care (London, England)
Jan 25, 2023
BACKGROUND: Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time ...
Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.
Academic radiology
May 27, 2022
RATIONALE AND OBJECTIVES: To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool.
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