Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Review Article

Interdisciplinary Collaboration Opportunities, Challenges, and Solutions for Artificial Intelligence in Ultrasound

Author(s): Qingrong Xia, Meng Du, Bin Li, Likang Hou and Zhiyi Chen*

Volume 18, Issue 10, 2022

Published on: 29 April, 2022

Article ID: e210322202461 Pages: 6

DOI: 10.2174/1573405618666220321123126

Price: $65

Abstract

Ultrasound is one of the most widely utilized imaging tools in clinical practice with the advantages of noninvasive nature and ease of use. However, ultrasound examinations have low reproducibility and considerable heterogeneity due to the variability of operators, scanners, and patients. Artificial Intelligence (AI)-assisted ultrasound has advanced in recent years, bringing it closer to routine clinical use. The combination of AI with ultrasound has opened up a world of possibilities for increasing work productivity and precision diagnostics. In this article, we describe AI strategies in ultrasound, from current opportunities, constraints to potential options for AI-assisted ultrasound.

Keywords: Artificial intelligence, ultrasound, deep learning, standardization, data security, interdisciplinary.

Graphical Abstract
[1]
Jha S, Topol EJ. Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA 2016; 316(22): 2353-4.
[http://dx.doi.org/10.1001/jama.2016.17438] [PMID: 27898975]
[2]
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2(10): 719-31.
[http://dx.doi.org/10.1038/s41551-018-0305-z] [PMID: 31015651]
[3]
Dilsizian SE, Siegel EL. Artificial intelligence in medicine and cardiac imaging: Harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep 2014; 16(1): 441.
[http://dx.doi.org/10.1007/s11886-013-0441-8] [PMID: 24338557]
[4]
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115-8.
[http://dx.doi.org/10.1038/nature21056] [PMID: 28117445]
[5]
Letterie G, Mac Donald A. Artificial intelligence in in vitro fertilization: A computer decision support system for day-to-day management of ovarian stimulation during in vitro fertilization. Fertil Steril 2020; 114(5): 1026-31.
[http://dx.doi.org/10.1016/j.fertnstert.2020.06.006]
[6]
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316(22): 2402-10.
[http://dx.doi.org/10.1001/jama.2016.17216] [PMID: 27898976]
[7]
Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-based radiological imaging for HCC: Current status and future of ultrasound. Diagnostics (Basel) 2021; 11(2): 292.
[http://dx.doi.org/10.3390/diagnostics11020292] [PMID: 33673229]
[8]
Santos MK, Ferreira Júnior JR, Wada DT, Tenório APM, Barbosa MHN, Marques PMA. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: Advances in imaging towards to precision medicine. Radiol Bras 2019; 52(6): 387-96.
[http://dx.doi.org/10.1590/0100-3984.2019.0049] [PMID: 32047333]
[9]
Ellahham S, Ellahham N, Simsekler MCE. Application of artificial intelligence in the health care safety context: Opportunities and challeng-es. Am J Med Qual 2020; 35(4): 341-8.
[http://dx.doi.org/10.1177/1062860619878515] [PMID: 31581790]
[10]
Ahmad HM, Khan MJ, Yousaf A, Ghuffar S, Khurshid K. Deep learning: A breakthrough in medical imaging. Curr Med Imaging Rev 2020; 16(8): 946-56.
[http://dx.doi.org/10.2174/1573405615666191219100824] [PMID: 33081657]
[11]
Patel VL, Shortliffe EH, Stefanelli M, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med 2009; 46(1): 5-17.
[http://dx.doi.org/10.1016/j.artmed.2008.07.017] [PMID: 18790621]
[12]
Zeng F, Liang X, Chen Z. New roles for clinicians in the age of artificial intelligence. BIO Integration 2020; 1(3): 113-7.
[http://dx.doi.org/10.15212/bioi-2020-0014]
[13]
Kuang M, Hu HT, Li W, Chen SL, Lu XZ. Articles that use artificial intelligence for ultrasound: A reader’s guide. Front Oncol 2021; 11: 631813.
[http://dx.doi.org/10.3389/fonc.2021.631813] [PMID: 34178622]
[14]
Komatsu M, Sakai A, Dozen A, et al. Towards clinical application of artificial intelligence in ultrasound imaging. Biomedicines 2021; 9(7): 720.
[http://dx.doi.org/10.3390/biomedicines9070720] [PMID: 34201827]
[15]
Olveres J, González G, Torres F, et al. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant Imaging Med Surg 2021; 11(8): 3830-53.
[http://dx.doi.org/10.21037/qims-20-1151] [PMID: 34341753]
[16]
Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017; 19(1): 221-48.
[http://dx.doi.org/10.1146/annurev-bioeng-071516-044442] [PMID: 28301734]
[17]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[18]
Kopelowitz E, Engelhard G. Lung nodules detection and segmentation using 3D Mask-RCNN. 2019.arXiv preprint arXiv:190707676
[19]
Akkus Z, Cai J, Boonrod A, et al. A survey of deep-learning applications in ultrasound: Artificial intelligence-powered ultrasound for im-proving clinical workflow. J Am Coll Radiol 2019; 16(9 Pt B): 1318-28.
[http://dx.doi.org/10.1016/j.jacr.2019.06.004] [PMID: 31492410]
[20]
Zhang J, Boora N, Melendez S, et al. Diagnostic accuracy of 3D ultrasound and artificial intelligence for detection of pediatric wrist injuries. Children (Basel) 2021; 8(6): 431.
[http://dx.doi.org/10.3390/children8060431] [PMID: 34063945]
[21]
Niu S, Huang J, Li J, et al. Differential diagnosis between small breast phyllodes tumors and fibroadenomas using artificial intelligence and ultrasound data. Quant Imaging Med Surg 2021; 11(5): 2052-61.
[http://dx.doi.org/10.21037/qims-20-919] [PMID: 33936986]
[22]
Parker LE. Creation of the national artificial intelligence research and development strategic plan. AI Mag 2018; 39(2): 25-31.
[http://dx.doi.org/10.1609/aimag.v39i2.2803]
[23]
Chungsik Yu. South Korea’s Strategic Culture and China’s National AI STRATEGY: A Neoclassical Realist View Robotics & AI Ethics 2021; 6.0(2.0)
[24]
Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics 2017; 73: 221-30.
[http://dx.doi.org/10.1016/j.ultras.2016.09.011] [PMID: 27668999]
[25]
Byra M, Galperin M, Ojeda-Fournier H, et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys 2019; 46(2): 746-55.
[http://dx.doi.org/10.1002/mp.13361] [PMID: 30589947]
[26]
Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016; 72: 150-7.
[http://dx.doi.org/10.1016/j.ultras.2016.08.004] [PMID: 27529139]
[27]
Meng D, Zhang L, Cao G, Cao W, Zhang G, Hu B. Liver fibrosis classification based on transfer learning and FCNet for ultrasound images. IEEE Access 2017; 5: 5804-10.
[http://dx.doi.org/10.1109/ACCESS.2017.2689058]
[28]
Vellido A. Societal issues concerning the application of artificial intelligence in medicine. Kidney Dis 2019; 5(1): 11-7.
[http://dx.doi.org/10.1159/000492428] [PMID: 30815459]
[29]
Yi PH, Hui FK, Ting DSW. Artificial intelligence and radiology: Collaboration is key. J Am Coll Radiol 2018; 15(5): 781-3.
[http://dx.doi.org/10.1016/j.jacr.2017.12.037] [PMID: 29398492]
[30]
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380(14): 1347-58.
[http://dx.doi.org/10.1056/NEJMra1814259] [PMID: 30943338]
[31]
Fan J, Han F, Liu H. Challenges of big data analysis. Natl Sci Rev 2014; 1(2): 293-314.
[http://dx.doi.org/10.1093/nsr/nwt032] [PMID: 25419469]
[32]
Shahid S, Ismawati J, Shamshul B, et al. Sentiment analysis of big data: Methods, applications, and open challenges. IEEE Access 2018; 6: 37807-27.
[http://dx.doi.org/10.1109/ACCESS.2018.2851311]
[33]
Fazal MI, Patel ME, Tye J, Gupta Y. The past, present and future role of artificial intelligence in imaging. Eur J Radiol 2018; 105: 246-50.
[http://dx.doi.org/10.1016/j.ejrad.2018.06.020] [PMID: 30017288]
[34]
Price WN II, Cohen IG. Privacy in the age of medical big data. Nat Med 2019; 25(1): 37-43.
[http://dx.doi.org/10.1038/s41591-018-0272-7] [PMID: 30617331]
[35]
Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med 2019; 25(1): 44-56.
[http://dx.doi.org/10.1038/s41591-018-0300-7] [PMID: 30617339]
[36]
Chen JH, Asch SM. Machine learning and prediction in medicine - beyond the peak of inflated expectations. N Engl J Med 2017; 376(26): 2507-9.
[http://dx.doi.org/10.1056/NEJMp1702071] [PMID: 28657867]
[37]
Chatila R, Firth-Butterflied K, Havens JC, Karachalios K. The IEEE global initiative for ethical considerations in artificial intelligence and autonomous systems. IEEE Robot Autom Mag 2017; 24(1): 110.
[http://dx.doi.org/10.1109/MRA.2017.2670225]
[38]
Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ 2018; 363: k4563.
[http://dx.doi.org/10.1136/bmj.k4563] [PMID: 30404897]
[39]
Mittelman M, Markham S, Taylor M. Patient commentary: Stop hyping artificial intelligence-patients will always need human doctors. BMJ 2018; 363: k4669.
[http://dx.doi.org/10.1136/bmj.k4669] [PMID: 30404859]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy