- Uncover This Tech Term: Uncertainty Quantification for Deep Learning
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Faghani S, Gamble C, Erickson BJ
- KMID: 2554293
- Korean J Radiol.
- 2024 Apr;25(4):395-398.
- doi: 10.3348/kjr.2024.0108
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- Caveats in Using Abnormality/Probability Scores from Artificial Intelligence Algorithms: Neither True Probability nor Level of Trustworthiness
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Park SH, Hwang EJ
- KMID: 2554286
- Korean J Radiol.
- 2024 Apr;25(4):328-330.
- doi: 10.3348/kjr.2024.0144
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- ChatGPT Vision for Radiological Interpretation: An Investigation Using Medical School Radiology Examinations
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Kim H, Kim P, Joo I, Kim JH, Park CM, Yoon SH
- KMID: 2554284
- Korean J Radiol.
- 2024 Apr;25(4):403-406.
- doi: 10.3348/kjr.2024.0017
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- Statistical Methods for the Analysis of Inter-Reader Agreement Among Three or More Readers
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Han K, Ryu L
- KMID: 2554285
- Korean J Radiol.
- 2024 Apr;25(4):325-327.
- doi: 10.3348/kjr.2023.0965
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- CT Demonstration of Local Cytokine-Release Syndrome Involving the Head and Neck Following Chimeric Antigen Receptor T Cell Infusion Therapy
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Ko JS, Lee JH, Yoon DH, Suh CH, Chung SR, Choi YJ, Baek JH
- KMID: 2554283
- Korean J Radiol.
- 2024 Apr;25(4):399-402.
- doi: 10.3348/kjr.2023.1100
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- 2024 Consensus Statement on Coronary Stenosis and Plaque Evaluation in CT Angiography From the Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT)
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Kim C, Park CH, Lee BY, Park CH, Kang EJ, Koo HJ, Kitagawa K, Cha MJ, Krittayaphong R, Choi SI, Yong HS, Ko SM, Kim SM, Hwang SH, Trang NN, Lee W, Kim YJ, Lee J, Yang DH
- KMID: 2554287
- Korean J Radiol.
- 2024 Apr;25(4):331-342.
- doi: 10.3348/kjr.2024.0112
The Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT) is an instructional initiative of the ASCI School designed to enhance educational standards. In 2021, the ASCI-PT was convened with the goal... -
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- Interpretation of Complete Tumor Response on MRI Following Chemoradiotherapy of Rectal Cancer: Inter-Reader Agreement and Associated Factors in Multi-Center Clinical Practice
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Kim HY, Cho SH, Jang JK, Kim B, Lee Cm, Lim JS, Moon SK, Oh SN, Seo N, Park SH
- KMID: 2554289
- Korean J Radiol.
- 2024 Apr;25(4):351-362.
- doi: 10.3348/kjr.2023.1213
Objective: To measure inter-reader agreement and identify associated factors in interpreting complete response (CR) on magnetic resonance imaging (MRI) following chemoradiotherapy (CRT) for rectal cancer. Materials and Methods: This retrospective study... -
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- Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study
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Kim DH, Seo J, Lee JH, Jeon ET, Jeong D, Chae HD, Lee E, Kang JH, Choi YH, Kim HJ, Chai JW
- KMID: 2554290
- Korean J Radiol.
- 2024 Apr;25(4):363-373.
- doi: 10.3348/kjr.2023.0671
Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult... -
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- Improving Diagnostic Performance of MRI for Temporal Lobe Epilepsy With Deep Learning-Based Image Reconstruction in Patients With Suspected Focal Epilepsy
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Suh PS, Park JE, Roh YH, Kim S, Jung M, Koo YS, Lee SA, Choi Y, Kim HS
- KMID: 2554291
- Korean J Radiol.
- 2024 Apr;25(4):374-383.
- doi: 10.3348/kjr.2023.0842
Objective: To evaluate the diagnostic performance and image quality of 1.5-mm slice thickness MRI with deep learningbased image reconstruction (1.5-mm MRI + DLR) compared to routine 3-mm slice thickness MRI... -
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- Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography
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Lee SE, Hong H, Kim EK
- KMID: 2554288
- Korean J Radiol.
- 2024 Apr;25(4):343-350.
- doi: 10.3348/kjr.2023.0907
Objective: Artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly used in mammography. While the continuous scores of AI-CAD have been related to malignancy risk, the understanding of how to interpret and... -
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- Dark-Blood Computed Tomography Angiography Combined With Deep Learning Reconstruction for Cervical Artery Wall Imaging in Takayasu Arteritis
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Su T, Zhang Z, Chen Y, Wang Y, Li Y, Xu M, Wang J, Li J, Tian X, Jin Z
- KMID: 2554292
- Korean J Radiol.
- 2024 Apr;25(4):384-394.
- doi: 10.3348/kjr.2023.1078
Objective: To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR),... -
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