Artificial Intelligence for the Measurement of the Aortic Valve Annulus

被引:26
|
作者
Thalappillil, Richard [1 ]
Datta, Pranav [1 ]
Datta, Saurabh [1 ]
Zhan, Yong [2 ]
Wells, Sophie [3 ]
Mahmood, Feroze [4 ]
Cobey, Frederick C. [1 ]
机构
[1] Tufts Med Ctr, Dept Anesthesiol & Perioperat Med, Div Cardiac Anesthesiol, Boston, MA 02111 USA
[2] Tufts Med Ctr, Dept Surg, Div Cardiac Surg, Boston, MA 02111 USA
[3] Tufts Med Ctr, Dept Med, Div Cardiol, Boston, MA 02111 USA
[4] Beth Israel Deaconess Med Ctr, Div Cardiac Anesthesiol, Boston, MA 02215 USA
关键词
artificial intelligence; automated software; machine learning; aortic valve three-dimensional modeling; CT;
D O I
10.1053/j.jvca.2019.06.017
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Objective: The authors aim to evaluate an automated echocardiography software as compared with computed tomography in measurement of the aortic valve annulus in patients with aortic stenosis. The authors hypothesize that aortic annular measurements by this software and computed tomography will show acceptable correlation. Design: This study is an Institutional Review Board-approved, retrospective data collection of patients with aortic stenosis who underwent implantation of a transcatheter heart valve with intraprocedural transesophageal echocardiography, multidetector computed tomography, and use of the Siemens eSie Valves automated aortic valve software. Setting: Intraprocedural in a single hospital institution. Participants: The participants are 47 patients who underwent implantation of an Edwards SAPIEN 3 transcatheter heart valve. Interventions: The authors compared aortic valve annulus measurements by two-dimensional transesophageal echocardiography, computed tomography, and the automated software. Measurements and Main Results: Aortic annulus measurements by the software correlated more closely to the computed tomography measurements than two-dimensional measurements. Bland-Altman analysis showed qualitative comparability of measurements performed by the automated software to computed tomography (95% limits of agreement between -4.62 mm and 1.26 mm for area-derived and -4.51 mm and 1.45 mm for perimeter-derived methods). Similarly, there was significant linear correlation with automated software use (r = 0.84, p < 0.0001 and r = 0.85, p<0.0001). Conclusions: Periprocedural aortic valve measurement by automated echocardiographic software correlates with computed tomography in patients with severe aortic stenosis. This technology is helpful and accurate, but has limitations. (C) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:65 / 71
页数:7
相关论文
共 50 条
  • [31] Artificial Intelligence in the Screening, Diagnosis, and Management of Aortic Stenosis
    Zhang, Yuxuan
    Wang, Moyang
    Zhang, Erli
    Wu, Yongjian
    REVIEWS IN CARDIOVASCULAR MEDICINE, 2024, 25 (01)
  • [32] Conformational Pulsatile Changes of the Aortic Annulus Impact on Prosthesis Sizing by Computed Tomography for Transcatheter Aortic Valve Replacement
    Blanke, Philipp
    Russe, Maximillian
    Leipsic, Jonathon
    Reinoehl, Jochen
    Ebersberger, Ullrich
    Suranyi, Pal
    Siepe, Matthias
    Pache, Gregor
    Langer, Mathias
    Schoepf, U. Joseph
    JACC-CARDIOVASCULAR INTERVENTIONS, 2012, 5 (09) : 984 - 994
  • [33] Artificial intelligence-enhanced strategies for reducing mortality in transcatheter aortic valve replacement: improving outcomes and minimizing risks
    Aiman, Ume
    Shahzad, Umer Bin
    Azad, Zainab
    Sheikh, Muhammad Ahmed
    EUROPEAN JOURNAL OF CARDIO-THORACIC SURGERY, 2024, 66 (04)
  • [34] Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model
    Kusunose, Masaya
    Inui, Atsuyuki
    Nishimoto, Hanako
    Mifune, Yutaka
    Yoshikawa, Tomoya
    Shinohara, Issei
    Furukawa, Takahiro
    Kato, Tatsuo
    Tanaka, Shuya
    Kuroda, Ryosuke
    SENSORS, 2023, 23 (14)
  • [35] Artificial intelligence based measurement system supervision
    Durakbasa, MN
    2002 FIRST INTERNATIONAL IEEE SYMPOSIUM INTELLIGENT SYSTEMS, VOL 1, PROCEEDINGS, 2002, : 298 - 301
  • [36] Artificial Intelligence and Educational Measurement: Opportunities and Threats
    Ho, Andrew D.
    JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, 2024, 49 (05) : 715 - 722
  • [37] Clinical Artificial Intelligence Applications Musculoskeletal
    Mutasa, Simukayi
    Yi, Paul H.
    RADIOLOGIC CLINICS OF NORTH AMERICA, 2021, 59 (06) : 1013 - 1026
  • [38] Radiomics and Artificial Intelligence in Pulmonary Fibrosis
    Chantzi, Stefania L.
    Kosvyra, Alexandra
    Chouvarda, Ioanna
    JOURNAL OF IMAGING INFORMATICS IN MEDICINE, 2025,
  • [39] A Basic Primer of Artificial Intelligence for Radiologists
    Stahl, Ethan
    Blumer, Steven L.
    CONTEMPORARY DIAGNOSTIC RADIOLOGY, 2022, 45 (01)
  • [40] Artificial intelligence in cardiac computed tomography
    Brandt, Verena
    Tesche, Christian
    KARDIOLOGE, 2021, 15 (06): : 655 - 668