Publications by authors named "Gyujun Jeong"
Background: The analysis of cardiovascular borders (CVBs) in chest x-rays (CXRs) traditionally relied on subjective assessment and does not have established normal ranges.
Objectives: The authors aimed to develop a deep learning-based method for quantifying CVBs on CXRs and to explore its clinical utility.
Methods: This study used a prevalidated deep learning to analyze CVBs.
Article Synopsis
- Coronary artery calcium (CAC) scoring CT is crucial for assessing heart disease risk, yet its effectiveness in patients with stents is unclear.
- The study introduces "StentFilter," an AI-based algorithm to automatically identify stents in CAC scans, aiming to enhance the efficiency of the CAC scoring process.
- Results showed StentFilter has a high sensitivity of 98.8% and a low false-positive rate, indicating it successfully distinguishes stented patients while minimizing errors in CAC scoring.
Deep learning model for intravascular ultrasound image segmentation with temporal consistency.
Int J Cardiovasc Imaging
November 2024
This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets.
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- Aortic dissection (AD) is a critical condition that needs quick and accurate diagnosis, and this study introduces a new method called ZOZI-seg for better segmentation of aorta in CT images.* -
- The ZOZI-seg method combines a 3D transformer and a 3D UNet, utilizing a two-stage architecture to capture both the overall anatomy and fine details of the aorta’s compartments: the true lumen, false lumen, and thrombosis.* -
- Results show that ZOZI-seg performs better than existing models with high segmentation accuracy, suggesting it could enhance AD diagnosis and treatment in clinical settings and warrants further research.*
Background And Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters.
Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets.
Objectives: Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD).
Methods: We developed CB_auto using 816 normal and 798 VHD CXRs.