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  1. Article: Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry.

    Kim, Kyung Won / Huh, Jimi / Urooj, Bushra / Lee, Jeongjin / Lee, Jinseok / Lee, In-Seob / Park, Hyesun / Na, Seongwon / Ko, Yousun

    Journal of gastric cancer

    2023  Volume 23, Issue 3, Page(s) 388–399

    Abstract: Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric- ... ...

    Abstract Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.
    Language English
    Publishing date 2023-08-08
    Publishing country Korea (South)
    Document type Journal Article ; Review
    ZDB-ID 2637180-7
    ISSN 2093-5641 ; 2093-582X
    ISSN (online) 2093-5641
    ISSN 2093-582X
    DOI 10.5230/jgc.2023.23.e30
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Mapping Changes in Glutamate with Glutamate-Weighted MRI in Forced Swim Test Model of Depression in Rats.

    Lee, Donghoon / Woo, Chul-Woong / Heo, Hwon / Ko, Yousun / Jang, Ji Sung / Na, Seongwon / Kim, Nari / Woo, Dong-Cheol / Kim, Kyung Won / Lee, Do-Wan

    Biomedicines

    2024  Volume 12, Issue 2

    Abstract: Chemical exchange saturation transfer with glutamate (GluCEST) imaging is a novel technique for the non-invasive detection and quantification of cerebral Glu levels in neuromolecular processes. Here we used GluCEST imaging ... ...

    Abstract Chemical exchange saturation transfer with glutamate (GluCEST) imaging is a novel technique for the non-invasive detection and quantification of cerebral Glu levels in neuromolecular processes. Here we used GluCEST imaging and
    Language English
    Publishing date 2024-02-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2720867-9
    ISSN 2227-9059
    ISSN 2227-9059
    DOI 10.3390/biomedicines12020384
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement.

    Na, Seongwon / Sung, Yu Sub / Ko, Yousun / Shin, Youngbin / Lee, Junghyun / Ha, Jiyeon / Ham, Su Jung / Yoon, Kyoungro / Kim, Kyung Won

    BMC medical imaging

    2022  Volume 22, Issue 1, Page(s) 87

    Abstract: Background: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the ... ...

    Abstract Background: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the predefined rules, is still performed manually by image analysts, which requires a lot of manpower and time.
    Methods: In this retrospective study, 1669 computed tomography (CT) images with five specific anatomical locations were collected from Asan Medical Center and Kangdong Sacred Heart Hospital. To generate the ground truth, two radiologists reviewed the anatomical locations and presence of contrast enhancement using the collected data. The individual deep learning model is developed through InceptionResNetv2 and transfer learning, and we propose Image Quality Check-Net (Image QC-Net), an ensemble AI model that utilizes it. To evaluate their clinical effectiveness, the overall accuracy and time spent on image quality check of a conventional model and ImageQC-net were compared.
    Results: ImageQC-net body part classification showed excellent performance in both internal (precision, 100%; recall, 100% accuracy, 100%) and external verification sets (precision, 99.8%; recovery rate, 99.8%, accuracy, 99.8%). In addition, contrast enhancement classification performance achieved 100% precision, recall, and accuracy in the internal verification set and achieved (precision, 100%; recall, 100%; accuracy 100%) in the external dataset. In the case of clinical effects, the reduction of time by checking the quality of artificial intelligence (AI) support by analysts 1 and 2 (49.7% and 48.3%, respectively) was statistically significant (p < 0.001).
    Conclusions: Comprehensive AI techniques to identify body parts and contrast enhancement on CT images are highly accurate and can significantly reduce the time spent on image quality checks.
    MeSH term(s) Artificial Intelligence ; Deep Learning ; Human Body ; Humans ; Retrospective Studies ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2022-05-13
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2061975-3
    ISSN 1471-2342 ; 1471-2342
    ISSN (online) 1471-2342
    ISSN 1471-2342
    DOI 10.1186/s12880-022-00815-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Sequence-Type Classification of Brain MRI for Acute Stroke Using a Self-Supervised Machine Learning Algorithm.

    Na, Seongwon / Ko, Yousun / Ham, Su Jung / Sung, Yu Sub / Kim, Mi-Hyun / Shin, Youngbin / Jung, Seung Chai / Ju, Chung / Kim, Byung Su / Yoon, Kyoungro / Kim, Kyung Won

    Diagnostics (Basel, Switzerland)

    2023  Volume 14, Issue 1

    Abstract: We propose a self-supervised machine learning (ML) algorithm for sequence-type classification of brain MRI using a supervisory signal from DICOM metadata (i.e., a rule-based virtual label). A total of 1787 brain MRI datasets were constructed, including ... ...

    Abstract We propose a self-supervised machine learning (ML) algorithm for sequence-type classification of brain MRI using a supervisory signal from DICOM metadata (i.e., a rule-based virtual label). A total of 1787 brain MRI datasets were constructed, including 1531 from hospitals and 256 from multi-center trial datasets. The ground truth (GT) was generated by two experienced image analysts and checked by a radiologist. An ML framework called ImageSort-net was developed using various features related to MRI acquisition parameters and used for training virtual labels and ML algorithms derived from rule-based labeling systems that act as labels for supervised learning. For the performance evaluation of ImageSort-net (ML
    Language English
    Publishing date 2023-12-27
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics14010070
    Database MEDical Literature Analysis and Retrieval System OnLINE

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