Download PDF
Semin intervent Radiol 2022; 39(03): 341-347
DOI: 10.1055/s-0042-1753524
Technical Corner

Artificial Intelligence in Interventional Radiology

Joseph R. Kallini
1   Department of Interventional Radiology, Ronald Reagan UCLA Medical Center, Los Angeles, California
,
John M. Moriarty
1   Department of Interventional Radiology, Ronald Reagan UCLA Medical Center, Los Angeles, California
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

The concept of artificial intelligence (AI) elicits futuristic images in the minds of many, perhaps reminding them of films about dystopian worlds. References to AI go back to the 1950s when British computer scientist Alan Turing developed the Church-Turing thesis, which states that natural numbers cannot be computed by a human being unless they are computable by a Turing machine (computer). Due to the fact that computers were not powerful at the time, applications were limited.[1]

John McCarthy, esteemed mathematician and computer scientist, coined the term “artificial intelligence” in 1956 during a highly attended summer workshop in Dartmouth. He complained that, “as soon as it works, no one calls it AI anymore.”[2] By 1959, machines began playing checkers better than humans. In 1965, McCarthy founded the Stanford Artificial Intelligence Laboratory, where groundbreaking research was conducted in computing and automation.[3]

AI took a back seat during the 1970s and 1980s (the so-called AI winters), but major breakthroughs occurred soon afterward. The 1990s were the dawn of faster computers and big data. In 1997, Deep Blue—a chess-playing supercomputer developed by International Business Machines (IBM) Corporation—defeated reigning world champion Gary Kasparov in a pair of six-game chess matches.[1] IBM later developed Watson in 2011, the supercomputer that defeated two reigning champions at Jeopardy! This was also the year that McCarthy passed away.

AI in radiology, though still in its infancy, has made tremendous strides. AI has been investigated in interventional radiology (IR) as well, though to a lesser extent. This article will summarize the role of AI in radiology and IR thus far and delve into its future applications.



Publication History

Article published online:
31 August 2022

© 2022. Thieme. All rights reserved.

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Charles R. , ed. Artificial intelligence and IR. 5th Annual Symposium on Academic Interventional Radiology; 2018 September 7–9; Washington, DC
    PubMed
  • 2 Mendel JVS. , ed. Learning AI from the experts: becoming an AI leader in global radiology (without needing a computer science degree) (Sponsored by the RSNA Committee of International Radiology Education). Radiological Society of North America 2019 Scientific Assembly and Annual Meeting; December 1–December 6, 2019; Chicago IL
    PubMed
  • 3 Museum CH. . John McCarthy 2020 [May 23, 2020]. Accessed June 16, 2022 at: https://computerhistory.org/profile/john-mccarthy/
    PubMed
  • 4 Andriole K. ed. Nuts and Bolts of Machine Learning and Artificial Intelligence. Chicago, IL: RSNA; 2019. December 1
    Search in Google Scholar
  • 5 Handelma GS, Kok HK, Chandra RV. et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol 2019; 212 (1): 38-43
    CrossrefPubMedSearch in Google Scholar
  • 6 Kitamura FC, ed AI in Healthcare: Advanced Topics. RSNA; Chicago, IL:
  • 7 Iezzi R, Goldberg SN, Merlino B, Posa A, Valentini V, Manfredi R. Artificial intelligence in interventional radiology: a literature review and future perspectives. J Oncol 2019; 2019: 6153041
    CrossrefPubMedSearch in Google Scholar
  • 8 Phung R. A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl Sci (Basel) 2019; 9: 4500
    CrossrefPubMedSearch in Google Scholar
  • 9 Erickson B. , ed. RSNA AI Deep Learning Lab: Beginner Class: Classification Task (Intro). Radiological Society of North America's 2019 Scientific Assembly and Annual Meeting; December 1–December 6, 2019; Chicago, IL
    PubMed
  • 10 Erickson BJ. , ed. RSNA AI Deep Learning Lab: Generative Adversarial Networks (GANs). Radiological Society of North America's 2019 Scientific Assembly and Annual Meeting; December 1–December 6, 2019; Chicago, IL
    PubMed
  • 11 Shih G. , ed. RSNA AI Deep Learning Lab: Segmentation. Radiological Society of North America's 2019 Scientific Assembly and Annual Meeting; December–December 6, 2019; Chicago, IL
    PubMed
  • 12 Society of Interventional Oncology. . Artificial Intelligence Hackathon at SIO2020
    PubMed
  • 13 Meek RD, Lungren MP, Gichoya JW. Machine learning for the interventional radiologist. AJR Am J Roentgenol 2019; 213 (04) 782-784
    CrossrefPubMedSearch in Google Scholar
  • 14 Monsky WL, Garza AS, Kim I. et al. Treatment planning and volumetric response assessment for yttrium-90 radioembolization: semiautomated determination of liver volume and volume of tumor necrosis in patients with hepatic malignancy. Cardiovasc Intervent Radiol 2011; 34 (02) 306-318
    CrossrefPubMedSearch in Google Scholar
  • 15 Cho H, Lee JG, Kang SJ. et al. Angiography-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions. J Am Heart Assoc 2019; 8 (04) e011685
    CrossrefPubMedSearch in Google Scholar
  • 16 Sundararajan SH, McClure TD, Winokur RS, Kishore SA, Madoff DC. Extrahepatic clinical application of vessel tracking software and 3D roadmapping tools: preliminary experience. J Vasc Interv Radiol 2019; 30 (07) 1021-1026
    CrossrefPubMedSearch in Google Scholar
  • 17 Monfardini L, Orsi F, Caserta R, Sallemi C, Della Vigna P, Bonomo G. et al. Ultrasound and cone beam CT fusion for liver ablation: technical note. Int J Hyperthermia 2018; 35 (01) 500-504
    CrossrefPubMedSearch in Google Scholar
  • 18 Chehab MA, Brinjikji W, Copelan A, Venkatesan AM. Navigational tools for interventional radiology and interventional oncology applications. Semin Intervent Radiol 2015; 32 (04) 416-427
    Thieme ConnectPubMedSearch in Google Scholar
  • 19 Wijnands TF, Ronot M, Gevers TJ. et al. Predictors of treatment response following aspiration sclerotherapy of hepatic cysts: an international pooled analysis of individual patient data. Eur Radiol 2017; 27 (02) 741-748
    CrossrefPubMedSearch in Google Scholar
  • 20 Kontogianni K, Russell K, Eberhardt R. et al. Clinical and quantitative computed tomography predictors of response to endobronchial lung volume reduction therapy using coils. Int J Chron Obstruct Pulmon Dis 2018; 13: 2215-2223
    CrossrefPubMedSearch in Google Scholar
  • 21 Gordic S, Corcuera-Solano I, Stueck A. et al. Evaluation of HCC response to locoregional therapy: validation of MRI-based response criteria versus explant pathology. J Hepatol 2017; 67 (06) 1213-1221
    CrossrefPubMedSearch in Google Scholar
  • 22 Lencioni R, Montal R, Torres F. et al. Objective response by mRECIST as a predictor and potential surrogate end-point of overall survival in advanced HCC. J Hepatol 2017; 66 (06) 1166-1172
    CrossrefPubMedSearch in Google Scholar
  • 23 Galastri FL, Nasser F, Affonso BB. et al. Imaging response predictors following drug eluting beads chemoembolization in the neoadjuvant liver transplant treatment of hepatocellular carcinoma. World J Hepatol 2020; 12 (01) 21-33
    CrossrefPubMedSearch in Google Scholar
  • 24 Kallini JR, Gabr A, Hickey R. et al. Indicators of lung shunt fraction determined by technetium-99 m macroaggregated albumin in patients with hepatocellular carcinoma. Cardiovasc Intervent Radiol 2017; 40 (08) 1213-1222
    CrossrefPubMedSearch in Google Scholar
  • 25 Abajian A, Murali N, Savic LJ. et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning - an artificial intelligence concept. J Vasc Interv Radiol 2018; 29 (06) 850-857.e1
    CrossrefPubMedSearch in Google Scholar
  • 26 Zhong BY, Ni CF, Ji JS. et al. Nomogram and artificial neural network for prognostic performance on the albumin-bilirubin grade for hepatocellular carcinoma undergoing transarterial chemoembolization. J Vasc Interv Radiol 2019; 30 (03) 330-338
    CrossrefPubMedSearch in Google Scholar
  • 27 Hricak H. 2016 new horizons lecture: beyond imaging-radiology of tomorrow. Radiology 2018; 286 (03) 764-775
    CrossrefPubMedSearch in Google Scholar
  • 28 Kassamali RH, Ladak B. The role of robotics in interventional radiology: current status. Quant Imaging Med Surg 2015; 5 (03) 340-343
    PubMedSearch in Google Scholar
  • 29 Bricault I, Zemiti N, Jouniaux E. et al. Light puncture robot for CT and MRI interventions: designing a new robotic architecture to perform abdominal and thoracic punctures. IEEE Eng Med Biol Mag 2008; 27 (03) 42-50
    CrossrefPubMedSearch in Google Scholar
  • 30 Neubach Z, Shoham M. Ultrasound-guided robot for flexible needle steering. IEEE Trans Biomed Eng 2010; 57 (04) 799-805
    CrossrefPubMedSearch in Google Scholar
  • 31 Stoianovici D, Cleary K, Patriciu A. et al. AcuBot: a robot for radiological interventions. IEEE Trans Robot Autom 2003; 19 (05) 927-930
    CrossrefPubMedSearch in Google Scholar
  • 32 Smilowitz NR, Weisz G. Robotic-assisted angioplasty: current status and future possibilities. Curr Cardiol Rep 2012; 14 (05) 642-646
    CrossrefPubMedSearch in Google Scholar
  • 33 Riga CV, Bicknell CD, Wallace D, Hamady M, Cheshire N. Robot-assisted antegrade in-situ fenestrated stent grafting. Cardiovasc Intervent Radiol 2009; 32 (03) 522-524
    CrossrefPubMedSearch in Google Scholar
  • 34 Riga CV, Cheshire NJ, Hamady MS, Bicknell CD. The role of robotic endovascular catheters in fenestrated stent grafting. J Vasc Surg 2010; 51 (04) 810-819 , discussion 819–820
    CrossrefPubMedSearch in Google Scholar
  • 35 Hiraki T, Kamegawa T, Matsuno T, Komaki T, Sakurai J, Kanazawa S. Zerobot®: a remote-controlled robot for needle insertion in CT-guided interventional radiology developed at Okayama University. Acta Med Okayama 2018; 72 (06) 539-546
    PubMedSearch in Google Scholar
  • 36 Appelbaum L, Mahgerefteh SY, Sosna J, Goldberg SN. Image-guided fusion and navigation: applications in tumor ablation. Tech Vasc Interv Radiol 2013; 16 (04) 287-295
    CrossrefPubMedSearch in Google Scholar
  • 37 Moser C, Becker J, Deli M, Busch M, Boehme M, Groenemeyer DH. A novel Laser Navigation System reduces radiation exposure and improves accuracy and workflow of CT-guided spinal interventions: a prospective, randomized, controlled, clinical trial in comparison to conventional freehand puncture. Eur J Radiol 2013; 82 (04) 627-632
    CrossrefPubMedSearch in Google Scholar
  • 38 Ritter M, Rassweiler MC, Häcker A, Michel MS. Laser-guided percutaneous kidney access with the Uro Dyna-CT: first experience of three-dimensional puncture planning with an ex vivo model. World J Urol 2013; 31 (05) 1147-1151
    CrossrefPubMedSearch in Google Scholar
  • 39 Mewes A, Hensen B, Wacker F, Hansen C. Touchless interaction with software in interventional radiology and surgery: a systematic literature review. Int J CARS 2017; 12 (02) 291-305
    CrossrefPubMedSearch in Google Scholar
  • 40 El-Shallaly GEH, Mohammed B, Muhtaseb MS, Hamouda AH, Nassar AHM. Voice recognition interfaces (VRI) optimize the utilization of theatre staff and time during laparoscopic cholecystectomy. Minim Invasive Ther Allied Technol 2005; 14 (06) 369-371
    CrossrefPubMedSearch in Google Scholar
  • 41 MedImaging.net. New VR Technology Puts Interventional Radiologists Inside 3D Blood Vessels.
    PubMed
  • 42 Chokshi FH. , ed. Artificial Intelligence and Precision Education: How AI Can Revolutionize Training in Radiology. Radiological Society of North America's 2019 Scientific Assembly and Annual Meeting; December 1–December 6, 2019; Chicago, IL
    PubMed
  • 43 Lau JW, Yang T, Toe KK, Huang W, Chang SK. Can robots accelerate the learning curve for surgical training? An analysis of residents and medical students. Ann Acad Med Singap 2018; 47 (01) 29-35
    CrossrefPubMedSearch in Google Scholar
  • 44 Awan O, Dey C, Salts H. et al. Making learning fun: gaming in radiology education. Acad Radiol 2019; 26 (08) 1127-1136
    CrossrefPubMedSearch in Google Scholar
  • 45 Bissonnette V, Mirchi N, Ledwos N, Alsidieri G, Winkler-Schwartz A, Del Maestro RF. Neurosurgical Simulation & Artificial Intelligence Learning Centre. Artificial intelligence distinguishes surgical training levels in a virtual reality spinal task. J Bone Joint Surg Am 2019; 101 (23) e127
    CrossrefPubMedSearch in Google Scholar
  • 46 Williamson DM, Xi X, Breyer FJ. A framework for evaluation and use of automated scoring. Educ Meas 2012; 31 (01) 2-13
    CrossrefPubMedSearch in Google Scholar
  • 47 Geis JR, Brady AP, Wu CC. et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. Radiology 2019; 293 (02) 436-440
    CrossrefPubMedSearch in Google Scholar
  • 48 Geis JR, Brady AP, Wu CC. et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. J Am Coll Radiol 2019; 16 (11) 1516-1521
    CrossrefPubMedSearch in Google Scholar
  • 49 Kahn CE. . Welcome to the Radiology: AI podcast [Internet]: RSNA2020 April 3. Podcast
    PubMed
  • 50 Gichoya JW, Kahn CE. , eds Ethics of AI in Radiology: Summary of the European and North American Multisociety Statement. RSNA; Chicago, IL: 2019. December 1
    Search in Google Scholar