AIMC Topic: Tomography, X-Ray Computed
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A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.
Medical physics
Sep 28, 2024
BACKGROUND: Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited.
Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.
Scientific reports
Sep 28, 2024
The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improv...
Label refinement network from synthetic error augmentation for medical image segmentation.
Medical image analysis
Sep 27, 2024
Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structure...
Using 3D point cloud and graph-based neural networks to improve the estimation of pulmonary function tests from chest CT.
Computers in biology and medicine
Sep 27, 2024
Pulmonary function tests (PFTs) are important clinical metrics to measure the severity of interstitial lung disease for systemic sclerosis patients. However, PFTs cannot always be performed by spirometry if there is a risk of disease transmission or ...
An open-source fine-tuned large language model for radiological impression generation: a multi-reader performance study.
BMC medical imaging
Sep 27, 2024
BACKGROUND: The impression section integrates key findings of a radiology report but can be subjective and variable. We sought to fine-tune and evaluate an open-source Large Language Model (LLM) in automatically generating impressions from the remain...
Automated ventricular segmentation and shunt failure detection using convolutional neural networks.
Scientific reports
Sep 27, 2024
While ventricular shunts are the main treatment for adult hydrocephalus, shunt malfunction remains a common problem that can be challenging to diagnose. Computer vision-derived algorithms present a potential solution. We designed a feasibility study ...
Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.
Nature communications
Sep 27, 2024
The standard method for identifying active Brown Adipose Tissue (BAT) is [F]-Fluorodeoxyglucose ([F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed w...
Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department.
Scientific reports
Sep 27, 2024
Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology can assist in interpreting non-enhanced brain CT scans, but its real-world impact on clinical decis...
Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.
Physics in medicine and biology
Sep 27, 2024
To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachyther...
Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods.
Journal of imaging informatics in medicine
Sep 26, 2024
With the increasing prevalence of mental illness, accurate clinical diagnosis of mental illness is crucial. Compared with MRI, CT has the advantages of wide application, low price, short scanning time, and high patient cooperation. This study aims to...
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