Artificial Intelligence With Deep Learning Enables Assessment of Aortic Aneurysm Diameter and Volume Through Different Computed Tomography Phases

被引：4
|
作者：
Coastaliou, Quentin [1 ]
Webster, Claire [2 ]
Bicknell, Colin [2 ]
Pouncey, Anna [2 ]
Ducasse, Eric [1 ]
Caradu, Caroline [1 ]
机构：
[1] Bordeaux Univ Hosp, Dept Vasc Surg, Bordeaux, France
[2] Imperial Coll London, Dept Vasc Surg, London, England
来源：
EUROPEAN JOURNAL OF VASCULAR AND ENDOVASCULAR SURGERY | 2024年 / 68卷 / 03期
关键词：
Abdominal aortic aneurysm; Automatic; segmentation; Deep learning; Endoleak; Endovascular aortic repair;
D O I：
10.1016/j.ejvs.2024.04.004
中图分类号：
R61 [外科手术学];
学科分类号：
摘要：
引用
收藏
页码：408 / 409
页数：2
相关论文
共 16 条
  • [1] Fully automatic volume segmentation of infrarenal abdominal aortic aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation
    Caradu, Caroline
    Spampinato, Benedetta
    Vrancianu, Ana Maria
    Berard, Xavier
    Ducasse, Eric
    JOURNAL OF VASCULAR SURGERY, 2021, 74 (01) : 246 - +
  • [2] Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging
    Abdolmanafi, Atefeh
    Forneris, Arianna
    Moore, Randy D. D.
    Di Martino, Elena S. S.
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2023, 9
  • [3] Deep Learning to Automatically Segment and Analyze Abdominal Aortic Aneurysm from Computed Tomography Angiography
    Brutti, Francesca
    Fantazzini, Alice
    Finotello, Alice
    Muller, Lucas Omar
    Auricchio, Ferdinando
    Pane, Bianca
    Spinella, Giovanni
    Conti, Michele
    CARDIOVASCULAR ENGINEERING AND TECHNOLOGY, 2022, 13 (04) : 535 - 547
  • [4] Deep Learning to Automatically Segment and Analyze Abdominal Aortic Aneurysm from Computed Tomography Angiography
    Francesca Brutti
    Alice Fantazzini
    Alice Finotello
    Lucas Omar Müller
    Ferdinando Auricchio
    Bianca Pane
    Giovanni Spinella
    Michele Conti
    Cardiovascular Engineering and Technology, 2022, 13 : 535 - 547
  • [5] External Validation of Fully-Automated Infrarenal Maximum Aortic Aneurysm Diameter Measurements in Computed Tomography Angiography Scans Using Artificial Intelligence (PRAEVAorta 2)
    Hatzl, Johannes
    Uhl, Christian
    Barb, Alexandru
    Henning, Daniel
    Fiering, Jonathan
    El-Sanosy, Edris
    Cuypers, Philippe W. M.
    Boeckler, Dittmar
    JOURNAL OF ENDOVASCULAR THERAPY, 2024,
  • [6] Deep Learning Artificial Intelligence for Single Photon Emission Computed Tomography: A Comprehensive Overview and Future Perspectives
    Cui, Rongyu
    Xie, Xin
    Fan, Long
    Yang, Xiaoli
    Ren, Yixuan
    Qin, Weihai
    2024 7TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA, ICAIBD 2024, 2024, : 416 - 421
  • [7] Artificial Intelligence Trained by Deep Learning Can Improve Computed Tomography Diagnosis of Nontraumatic Subarachnoid Hemorrhage by Nonspecialists
    Nishi, Toru
    Yamashiro, Shigeo
    Okumura, Shuichiro
    Takei, Mizuki
    Tachibana, Atsushi
    Akahori, Sadato
    Kaji, Masatomo
    Uekawa, Ken
    Amadatsu, Toshihiro
    NEUROLOGIA MEDICO-CHIRURGICA, 2021, 61 (11) : 652 - 660
  • [8] Contribution of artificial intelligence applications developed with the deep learning method to the diagnosis of COVID-19 pneumonia on computed tomography
    Aydin, Nevin
    Celik, Ozer
    TUBERKULOZ VE TORAKS-TUBERCULOSIS AND THORAX, 2021, 69 (04): : 486 - 491
  • [9] Use of Artificial Intelligence Deep Learning to Determine the Malignant Potential of Pancreatic Cystic Neoplasms With Preoperative Computed Tomography Imaging
    Watson, Michael D.
    Lyman, William B.
    Passeri, Michael J.
    Murphy, Keith J.
    Sarantou, John P.
    Iannitti, David A.
    Martinie, John B.
    Vrochides, Dionisios
    Baker, Erin H.
    AMERICAN SURGEON, 2021, 87 (04) : 602 - 607
  • [10] Automatic diagnosis of COVID-19 pneumonia using artificial intelligence deep learning algorithm based on lung computed tomography images
    Amiri, Mohammad
    Ranjbar, Manizheh
    Mohammadi, Gholamreza Fallah
    JOURNAL OF MEDICAL SIGNALS & SENSORS, 2023, 13 (02): : 110 - 117
12