A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

被引:3
作者
Mervak, Benjamin M. [1 ]
Fried, Jessica G. [1 ]
Wasnik, Ashish P. [1 ]
机构
[1] Univ Michigan, Michigan Med, Dept Radiol, 1500 E Med Ctr Dr, Ann Arbor, MI 48109 USA
关键词
artificial intelligence; machine learning; deep learning; radiology; abdominal imaging; body imaging; MRI; CT; US; COMPUTED-TOMOGRAPHY; SEGMENTATION; CANCER; CT; RADIOLOGY; SARCOPENIA; CARCINOMA; DIAGNOSIS; IMAGES; RISK;
D O I
10.3390/diagnostics13182889
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.
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页数:13
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共 101 条
  • [1] Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning
    Abel, Lorraine
    Wasserthal, Jakob
    Weikert, Thomas
    Sauter, Alexander W.
    Nesic, Ivan
    Obradovic, Marko
    Yang, Shan
    Manneck, Sebastian
    Glessgen, Carl
    Ospel, Johanna M.
    Stieltjes, Bram
    Boll, Daniel T.
    Friebe, Bjoern
    [J]. DIAGNOSTICS, 2021, 11 (05)
  • [2] Association Between Visceral and Subcutaneous Adipose Depots and Incident Cardiovascular Disease Risk Factors
    Abraham, Tobin M.
    Pedley, Alison
    Massaro, Joseph M.
    Hoffmann, Udo
    Fox, Caroline S.
    [J]. CIRCULATION, 2015, 132 (17) : 1639 - 1647
  • [3] AI for Radiology, about us
  • [4] Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation
    Alcala Mata, Lidia
    Antonio Retamero, Juan
    Gupta, Rajan T.
    Garcia Figueras, Roberto
    Luna, Antonio
    [J]. RADIOGRAPHICS, 2021, 41 (06) : 1676 - 1697
  • [5] American College of Radiology Data Science Institute, About us
  • [6] [Anonymous], Artificial Intelligence and Machine Learning in Software as a Medical Device
  • [7] [Anonymous], 2014, Software as a Medical Device": Possible Framework for Risk Categorization and Corresponding Considerations
  • [8] Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art
    Barat, Maxime
    Cottereau, Anne-Segolene
    Gaujoux, Sebastien
    Tenenbaum, Florence
    Sibony, Mathilde
    Bertherat, Jerome
    Libe, Rossella
    Gaillard, Martin
    Jouinot, Anne
    Assie, Guillaume
    Hoeffel, Christine
    Soyer, Philippe
    Dohan, Anthony
    [J]. CANCERS, 2022, 14 (03)
  • [9] Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning
    Bardis, Michelle
    Houshyar, Roozbeh
    Chantaduly, Chanon
    Tran-Harding, Karen
    Ushinsky, Alexander
    Chahine, Chantal
    Rupasinghe, Mark
    Chow, Daniel
    Chang, Peter
    [J]. RADIOLOGY-IMAGING CANCER, 2021, 3 (03):
  • [10] Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis
    Bedrikovetski, Sergei
    Seow, Warren
    Kroon, Hidde M.
    Traeger, Luke
    Moore, James W.
    Sammour, Tarik
    [J]. EUROPEAN JOURNAL OF RADIOLOGY, 2022, 149