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DOI: 10.1055/s-0041-1736659
Challenges of Implementing Artificial Intelligence in Interventional Radiology
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Abstract
Artificial intelligence (AI) and deep learning (DL) remains a hot topic in medicine. DL is a subcategory of machine learning that takes advantage of multiple layers of interconnected neurons capable of analyzing immense amounts of data and “learning” patterns and offering predictions. It appears to be poised to fundamentally transform and help advance the field of diagnostic radiology, as heralded by numerous published use cases and number of FDA-cleared products. On the other hand, while multiple publications have touched upon many great hypothetical use cases of AI in interventional radiology (IR), the actual implementation of AI in IR clinical practice has been slow compared with the diagnostic world. In this article, we set out to examine a few challenges contributing to this scarcity of AI applications in IR, including inherent specialty challenges, regulatory hurdles, intellectual property, raising capital, and ethics. Owing to the complexities involved in implementing AI in IR, it is likely that IR will be one of the late beneficiaries of AI. In the meantime, it would be worthwhile to continuously engage in defining clinically relevant use cases and focus our limited resources on those that would benefit our patients the most.
Keywords
artificial intelligence - machine learning - interventional radiology - use cases - challengesPublication History
Article published online:
24 November 2021
© 2021. Thieme. All rights reserved.
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References
- 1 Artificial Intelligence in Healthcare Market by Offering. Technology, End-Use Application, End User | COVID-19 Impact Analysis | MarketsandMarkets™. Accessed July 10, 2021. Accessed September 30, 2021 at: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html
- 2 Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. Radiographics 2017; 37 (02) 505-515
- 3 Dale R. Law and word order: NLP in legal tech. Nat Lang Eng 2019; 25 (01) 211-217
- 4 van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 2021; 31 (06) 3797-3804
- 5 Rezazade Mehrizi MH, van Ooijen P, Homan M. Applications of artificial intelligence (AI) in diagnostic radiology: a technography study. Eur Radiol 2021; 31 (04) 1805-1811
- 6 Jin D, Harrison AP, Zhang L. et al. Artificial intelligence in radiology. In: Xing L, Giger ML, Min JK. eds. Artificial Intelligence in Medicine. Academic Press; 2021: 265-289
- 7 Lakhani P, Prater AB, Hutson RK. et al. Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol 2018; 15 (02) 350-359
- 8 Alexander A, Jiang A, Ferreira C, Zurkiya D. An intelligent future for medical imaging: a market outlook on artificial intelligence for medical imaging. J Am Coll Radiol 2020; 17 (1, Pt B; 1, Part B): 165-170
- 9 Desai SB, Pareek A, Lungren MP. Current and emerging artificial intelligence applications for pediatric interventional radiology. Pediatr Radiol 2021; DOI: 10.1007/s00247-021-05013-y.
- 10 Gurgitano M, Angileri SA, Rodà GM. et al. Interventional radiology ex-machina: impact of artificial intelligence on practice. Radiol Med (Torino) 2021; 126 (07) 998-1006
- 11 Letzen B, Wang CJ, Chapiro J. The role of artificial intelligence in interventional oncology: a primer. J Vasc Interv Radiol 2019; 30 (01) 38-41.e1
- 12 Zhou X-Y, Guo Y, Shen M, Yang G-Z. Application of artificial intelligence in surgery. Front Med 2020; 14 (04) 417-430
- 13 Bodenstedt S, Wagner M, Müller-Stich BP, Weitz J, Speidel S. Artificial intelligence-assisted surgery: potential and challenges. Visc Med 2020; 36 (06) 450-455
- 14 Sardar P, Abbott JD, Kundu A, Aronow HD, Granada JF, Giri J. Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. JACC Cardiovasc Interv 2019; 12 (14) 1293-1303
- 15 Birkhoff DC, van Dalen ASHM, Schijven MP. A review on the current applications of artificial intelligence in the operating room. Surg Innov Published online February 24, 2021; 1553350621996961 DOI: 10.1177/1553350621996961.
- 16 ACR Data Science Institute. Define-AI Use Case Directory. Accessed July 11, 2021. Accessed September 30, 2021 at: https://www.acrdsi.org/DSI-Services/Define-AI
- 17 D'Amore B, Smolinski-Zhao S, Daye D, Uppot RN. Role of machine learning and artificial intelligence in interventional oncology. Curr Oncol Rep 2021; 23 (06) 70
- 18 Pesapane F, Tantrige P, Patella F. et al. Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists. Med Oncol 2020; 37 (05) 40
- 19 Meek RD, Lungren MP, Gichoya JW. Machine learning for the interventional radiologist. AJR Am J Roentgenol 2019; 213 (04) 782-784
- 20 Geschwind JH, Hochster HS. Tools from the World of Artificial Intelligence in Interventional Oncology: be careful what you wish for. J Vasc Interv Radiol 2019; 30 (03) 339-341
- 21 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
- 22 Shah NA, Hawkins CM. Decreasing Outpatient Pre-procedure Wait Times in a Pediatric Interventional Radiology (IR) Department: A Software-Solution Enabled Quality Improvement Project. Presented at the: RSNA. Accessed July 15, 2021 at: https://www.rsna.org/uploadedfiles/rsna/content/science/quality/storyboards/2015/shah_qs101.pdf
- 23 Choi GH, Yun J, Choi J. et al. Development of machine learning-based clinical decision support system for hepatocellular carcinoma. Sci Rep 2020; 10 (01) 14855
- 24 Zhong B-Y, Ni C-F, Ji J-S. 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
- 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
- 26 Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine learning offers exciting potential for predicting postprocedural outcomes: a framework for developing random forest models in IR. J Vasc Interv Radiol 2020; 31 (06) 1018-1024.e4
- 27 Ingrisch M, Schöppe F, Paprottka K. et al. Prediction of 90Y radioembolization outcome from pretherapeutic factors with random survival forests. J Nucl Med 2018; 59 (05) 769-773
- 28 Daye D, Staziaki PV, Furtado VF. et al. CT texture analysis and machine learning improve post-ablation prognostication in patients with adrenal metastases: a proof of concept. Cardiovasc Intervent Radiol 2019; 42 (12) 1771-1776
- 29 Morshid A, Elsayes KM, Khalaf AM. et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell 2019; 1 (05) e180021
- 30 Seals K, Al-Hakim R, Mulligan P. et al. The development of a machine learning smart speaker application for device sizing in interventional radiology. Journal of Vascular and Interventional Radiology 2019; 30: S20 https://doi.org/10.1016/j.jvir.2018.12.077
- 31 Lee AR, Cho Y, Jin S, Kim N. Enhancement of surgical hand gesture recognition using a capsule network for a contactless interface in the operating room. Comput Methods Programs Biomed 2020; 190: 105385
- 32 Pereme F, Flores JZ, Scavazzin M, Valentini F, Radoux J-P. Conception of a touchless human machine interaction system for operating rooms using deep learning. In: Optics, Photonics, and Digital Technologies for Imaging Applications V. Vol 10679. International Society for Optics and Photonics; 2018: 106790R DOI: 10.1117/12.2319141
- 33 Zimmermann JM, Vicentini L, Van Story D. et al. Quantification of avoidable radiation exposure in interventional fluoroscopy with eye tracking technology. Invest Radiol 2020; 55 (07) 457-462
- 34 Bang JY, Hough M, Hawes RH, Varadarajulu S. Use of artificial intelligence to reduce radiation exposure at fluoroscopy-guided endoscopic procedures. Am J Gastroenterol 2020; 115 (04) 555-561
- 35 Fagogenis G, Mencattelli M, Machaidze Z. et al. Autonomous robotic intracardiac catheter navigation using haptic vision. Sci Robot 2019; 4 (29) eaaw1977 DOI: 10.1126/scirobotics.aaw1977.
- 36 Molony D, Samady H. TCT-342 DeepIVUS: a machine learning platform for fully automatic IVUS segmentation and phenotyping. J Am Coll Cardiol 2019; 74 (13, Suppl): B339
- 37 Karstensen L, Behr T, Pusch TP, Mathis-Ullrich F, Stallkamp J. Autonomous guidewire navigation in a two dimensional vascular phantom. Curr Dir Biomed Eng 2020; 6 (01) DOI: 10.1515/cdbme-2020-0007.
- 38 Auloge P, Cazzato RL, Ramamurthy N. et al. Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial. Eur Spine J 2020; 29 (07) 1580-1589
- 39 Liang X, Yang X, Yin S. et al. Artificial intelligence in plastic surgery: applications and challenges. Aesthetic Plast Surg 2021; 45 (02) 784-790
- 40 Tranter-Entwistle I, Wang H, Daly K, Maxwell S, Connor S. The challenges of implementing artificial intelligence into surgical practice. World J Surg 2021; 45 (02) 420-428
- 41 Viz.ai's stroke-detecting CDS app receives FDA nod. MobiHealthNews. Published February 14, 2018. Accessed August 1, 2021 at: https://www.mobihealthnews.com/content/vizai%E2%80%99s-stroke-detecting-cds-app-receives-fda-nod
- 42 U.S. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback. Accessed October 21, 2021 at: https://www.fda.gov/media/145022/download
- 43 U.S. Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan. Published online December 1, 2021. Accessed July 14, 2021 at: https://www.fda.gov/news-events/press-announcements/fda-releases-artificial-intelligencemachine-learning-action-plan
- 44 Penumbra. Penumbra's Newest Generation of Indigo Aspiration System Receives FDA Clearance for Pulmonary Embolism. Accessed July 14, 2021 at: https://investors.penumbrainc.com/investors-relations/press-releases/press-release-details/2020/Penumbras-Newest-Generation-of-Indigo-Aspiration-System-Receives-FDA-Clearance-for-Pulmonary-Embolism/default.aspx
- 45 Siemens Healthineers. Siemens Healthineers Receives FDA Clearance for TrueFusion Structural Heart Disease Feature. Accessed July 12, 2021 at: https://www.siemens-healthineers.com/en-us/news/fdaclearanceoftruefusionfeature.html
- 46 World Intellectual Property Organization, 2019. Technology Trends 2019 – Artificial Intelligence. Accessed October 21, 2021 at: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
- 47 United States Patent and Trademark Office. Revised Patent Subject Matter Eligibility Guidance. Published online July 1, 2019. Accessed September 30, 2021 at: https://www.govinfo.gov/content/pkg/FR-2019-01-07/pdf/2018-28282.pdf
- 48 United States Patent and Trademark Office. Patent Eligibility Guidance Update. Published online October 2019. Accessed September 30, 2021 at: https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_update.pdf
- 49 Inventions Patentable. 35 U.S. Code § 101
- 50 Lizarralde M. A Guideline to Artificial Intelligence, Machine Learning and Intellectual Property. Published online September 15, 2020. Accessed September 30, 2021 at: https://www.4ipcouncil.com/application/files/9016/0017/8691/A_Guideline_to_Artificial_Intelligence_Machine_Learning_and_Intellectual_Property.pdf
- 51 Signify Research. VC-Funding for Medical Imaging AI Companies Tops $2.6 Billion. Signify Research. Published March 8, 2021. Accessed June 6, 2021 at: https://www.signifyresearch.net/medical-imaging/vc-funding-for-medical-imaging-ai-companies-tops-2-6-billion/
- 52 Geis JR, Brady A, Wu CC. et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 2019; 10 (01) 101
- 53 DeepMind. Perceiver: General Perception with Iterative Attention. DeepMind. Accessed July 15, 2021 at: https://deepmind.com/research/publications/Perceiver-General-Perception-with-Iterative-Attention