WFUMB Commentary Paper on Artificial intelligence in Medical Ultrasound Imaging

被引:0
作者
Cui, Xin Wu [1 ,2 ]
Goudie, Adrian [3 ]
Blaivas, Michael [4 ]
Chai, Young Jun [5 ]
Chammas, Maria Cristina [6 ]
Dong, Yi [7 ]
Stewart, Jonathon [8 ]
Jiang, Tian-An [9 ]
Liang, Ping [10 ]
Sehgal, Chandra M. [11 ]
Wu, Xing-Long [12 ]
Hsieh, Peter Ching-Chang [13 ]
Adrian, Saftoiu [14 ]
Dietrich, Christoph F. [15 ]
机构
[1] Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Med Ultrasound, Wuhan, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, State Key Lab Diag & Treatment Severe Zoonot Infec, Wuhan, Hubei, Peoples R China
[3] Fiona Stanley Hosp, Dept Emergency, Perth, Australia
[4] Univ South Carolina, Sch Med, Dept Med, Columbia, SC USA
[5] Seoul Natl Univ, Seoul Metropolitan Govt, Boramae Med Ctr, Dept Surg,Coll Med, Seoul, South Korea
[6] Univ Sao Paulo, Hosp Clin, Fac Med, Sao Paulo, Brazil
[7] Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Dept Ultrasound, Shanghai, Peoples R China
[8] Univ Western Australia, Sch Med, Perth, WA, Australia
[9] Zhejiang Univ, Affiliated Hosp 1, Dept Ultrasound Med, Sch Med, Hangzhou, Zhejiang, Peoples R China
[10] Chinese Peoples Liberat Army Gen Hosp, Dept Intervent Ultrasound, Beijing, Peoples R China
[11] Univ Penn, Dept Radiol, Ultrasound Res Lab, Philadelphia, PA USA
[12] Wuhan Inst Technol, Sch Comp Sci & Engn, Wuhan, Hubei, Peoples R China
[13] Chang Gung Mem Hosp, Taipei, Taiwan
[14] Univ Med & Pharm Craiova, Res Ctr Gastroenterol & Hepatol, Craiova, Romania
[15] Hosp Hirslanden Bern Beau Site Salem & Permanence, Dept Gen Internal Med DAIM, Bern, Switzerland
关键词
Ultrasound; Point-of-care ultrasound; Contrast-enhanced ultrasound; Artificial intelligence; Machine learning; Deep learning; DEEP NEURAL-NETWORK; AUTOMATIC DETECTION; 3D ULTRASOUND; SEGMENTATION; CLASSIFICATION; DIAGNOSIS; RECOGNITION; IMAGES; QUANTIFICATION; FEASIBILITY;
D O I
10.1016/j.ultrasmedbio.2024.10.016
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Artificial intelligence (AI) is defined as the theory and development of computer systems able to perform tasks normally associated with human intelligence. At present, AI has been widely used in a variety of ultrasound tasks, including in point-of-care ultrasound, echocardiography, and various diseases of different organs. However, the characteristics of ultrasound, compared to other imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), poses significant additional challenges to AI. Application of AI can not only reduce variability during ultrasound image acquisition, but can standardize these interpretations and identify patterns that escape the human eye and brain. These advances have enabled greater innovations in ultrasound AI applications that can be applied to a variety of clinical settings and disease states. Therefore, The World Federation of Ultrasound in Medicine and Biology (WFUMB) is addressing the topic with a brief and practical overview of current and potential future AI applications in medical ultrasound, as well as discuss some current limitations and future challenges to AI implementation.
引用
收藏
页码:428 / 438
页数:11
相关论文
共 186 条
  • [1] Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks
    Abdolali, Fatemeh
    Kapur, Jeevesh
    Jaremko, Jacob L.
    Noga, Michelle
    Hareendranathan, Abhilash R.
    Punithakumar, Kumaradevan
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 122
  • [2] A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy
    Abu Anas, Emran Mohammad
    Mousavi, Parvin
    Abolmaesumi, Purang
    [J]. MEDICAL IMAGE ANALYSIS, 2018, 48 : 107 - 116
  • [3] Using Artificial Intelligence to Label Free-Text Operative and Ultrasound Reports for Grading Pediatric Appendicitis
    Abu-Ashour, Waseem
    Emil, Sherif
    Poenaru, Dan
    [J]. JOURNAL OF PEDIATRIC SURGERY, 2024, 59 (05) : 783 - 790
  • [4] Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms
    Acharya, U. R.
    Faust, O.
    Sree, S. V.
    Molinari, F.
    Garberoglio, R.
    Suri, J. S.
    [J]. TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2011, 10 (04) : 371 - 380
  • [5] Aditya B, 2020, Proc SPIE, V11315
  • [6] Integration of Physical Examination, Old and New Biomarkers, and Ultrasonography by Using Neural Networks for Pediatric Appendicitis
    Akgul, Fatma
    Er, Anil
    Ulusoy, Emel
    Caglar, Aykut
    Citlenbik, Hale
    Keskinoglu, Pembe
    Sisman, Ali R.
    Karakus, Osman Z.
    Ozer, Erdener
    Duman, Murat
    Yilmaz, Durgul
    [J]. PEDIATRIC EMERGENCY CARE, 2021, 37 (12) : E1075 - E1081
  • [7] Neural networks for automatic scoring of arthritis disease activity on ultrasound images
    Andersen, Jakob Kristian Holm
    Pedersen, Jannik Skyttegaard
    Laursen, Martin Sundahl
    Holtz, Kathrine
    Grauslund, Jakob
    Savarimuthu, Thiusius Rajeeth
    Just, Soren Andreas
    [J]. RMD OPEN, 2019, 5 (01):
  • [8] A preliminary application of intraoral Doppler ultrasound images to deep learning techniques for predicting late cervical lymph node metastasis in early tongue cancers
    Ariji, Yoshiko
    Fukuda, Motoki
    Kise, Yoshitaka
    Nozawa, Michihito
    Nagao, Toru
    Nakayama, Atsushi
    Sugita, Yoshihiko
    Katumata, Akitoshi
    Ariji, Eiichiro
    [J]. ORAL SCIENCE INTERNATIONAL, 2020, 17 (02) : 59 - 66
  • [9] Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study
    Arntfield, Robert
    VanBerlo, Blake
    Alaifan, Thamer
    Phelps, Nathan
    White, Matthew
    Chaudhary, Rushil
    Ho, Jordan
    Wu, Derek
    [J]. BMJ OPEN, 2021, 11 (03):
  • [10] Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert
    Asch, Federico M.
    Poilvert, Nicolas
    Abraham, Theodore
    Jankowski, Madeline
    Cleve, Jayne
    Adams, Michael
    Romano, Nathanael
    Hong, Ha
    Mor-Avi, Victor
    Martin, Randolph P.
    Lang, Roberto M.
    [J]. CIRCULATION-CARDIOVASCULAR IMAGING, 2019, 12 (09)