Artificial Intelligence in Interventional Radiology: State of the Art

Overview

Journal Eur Radiol Exp

Date 2024 May 1

PMID 38693468

Authors

Pierluigi Glielmo

Stefano Fusco

Salvatore Gitto

Giulia Zantonelli

Domenico Albano

Carmelo Messina

Luca Maria Sconfienza

Giovanni Mauri

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

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References

Calandri M, Mauri G, Yevich S, Gazzera C, Basile D, Gatti M . Fusion Imaging and Virtual Navigation to Guide Percutaneous Thermal Ablation of Hepatocellular Carcinoma: A Review of the Literature. Cardiovasc Intervent Radiol. 2019; 42(5):639-647. DOI: 10.1007/s00270-019-02167-z. View

Fritz J, U-Thainual P, Ungi T, Flammang A, Kathuria S, Fichtinger G . MR-guided vertebroplasty with augmented reality image overlay navigation. Cardiovasc Intervent Radiol. 2014; 37(6):1589-96. DOI: 10.1007/s00270-014-0885-2. View

Monfardini L, Orsi F, Caserta R, Sallemi C, Della Vigna P, Bonomo G . Ultrasound and cone beam CT fusion for liver ablation: technical note. Int J Hyperthermia. 2018; 35(1):500-504. DOI: 10.1080/02656736.2018.1509237. View

Mauri G, Cova L, De Beni S, Ierace T, Tondolo T, Cerri A . Real-time US-CT/MRI image fusion for guidance of thermal ablation of liver tumors undetectable with US: results in 295 cases. Cardiovasc Intervent Radiol. 2014; 38(1):143-51. DOI: 10.1007/s00270-014-0897-y. View

Nalepa J, Marcinkiewicz M, Kawulok M . Data Augmentation for Brain-Tumor Segmentation: A Review. Front Comput Neurosci. 2020; 13:83. PMC: 6917660. DOI: 10.3389/fncom.2019.00083. View

Pinnock M, Hu Y, Bandula S, Barratt D . Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy. Int J Comput Assist Radiol Surg. 2023; 18(8):1437-1449. PMC: 10363071. DOI: 10.1007/s11548-023-02843-z. View

Uppot R, Laguna B, McCarthy C, Novi G, Phelps A, Siegel E . Implementing Virtual and Augmented Reality Tools for Radiology Education and Training, Communication, and Clinical Care. Radiology. 2019; 291(3):570-580. DOI: 10.1148/radiol.2019182210. View

Mauri G, Gennaro N, De Beni S, Ierace T, Goldberg S, Rodari M . Real-Time US-FDG-PET/CT Image Fusion for Guidance of Thermal Ablation of FDG-PET-Positive Liver Metastases: The Added Value of Contrast Enhancement. Cardiovasc Intervent Radiol. 2018; 42(1):60-68. DOI: 10.1007/s00270-018-2082-1. View

Fritz J, U-Thainual P, Ungi T, Flammang A, Fichtinger G, Iordachita I . Augmented reality visualisation using an image overlay system for MR-guided interventions: technical performance of spine injection procedures in human cadavers at 1.5 Tesla. Eur Radiol. 2012; 23(1):235-45. DOI: 10.1007/s00330-012-2569-0. View

10.

Nielsen M, Waldmann M, Frolich A, Flottmann F, Hristova E, Bendszus M . Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study. Stroke. 2021; 52(11):3497-3504. DOI: 10.1161/STROKEAHA.120.033807. View

11.

Abajian A, Murali N, Savic L, Laage-Gaupp F, Nezami N, Duncan J . 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(6):850-857.e1. PMC: 5970021. DOI: 10.1016/j.jvir.2018.01.769. View

12.

Solbiati M, Ierace T, Muglia R, Pedicini V, Iezzi R, Passera K . Thermal Ablation of Liver Tumors Guided by Augmented Reality: An Initial Clinical Experience. Cancers (Basel). 2022; 14(5). PMC: 8909771. DOI: 10.3390/cancers14051312. View

13.

Malpani R, Petty C, Yang J, Bhatt N, Zeevi T, Chockalingam V . Quantitative Automated Segmentation of Lipiodol Deposits on Cone-Beam CT Imaging Acquired during Transarterial Chemoembolization for Liver Tumors: A Deep Learning Approach. J Vasc Interv Radiol. 2021; 33(3):324-332.e2. PMC: 8972393. DOI: 10.1016/j.jvir.2021.12.017. View

14.

Mauri G, Mistretta F, Bonomo G, Camisassi N, Conti A, Della Vigna P . Long-Term Follow-Up Outcomes after Percutaneous US/CT-Guided Radiofrequency Ablation for cT1a-b Renal Masses: Experience from Single High-Volume Referral Center. Cancers (Basel). 2020; 12(5). PMC: 7281086. DOI: 10.3390/cancers12051183. View

15.

Arthur A, Johnston E, Winfield J, Blackledge M, Jones R, Huang P . Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?. Front Oncol. 2022; 12:892620. PMC: 9286756. DOI: 10.3389/fonc.2022.892620. View

16.

Schwarz L, Bigdelou A, Navab N . Learning gestures for customizable human-computer interaction in the operating room. Med Image Comput Comput Assist Interv. 2011; 14(Pt 1):129-36. DOI: 10.1007/978-3-642-23623-5_17. View

17.

Arapi V, Hardt-Stremayr A, Weiss S, Steinbrener J . Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions. Eur Radiol Exp. 2023; 7(1):30. PMC: 10277269. DOI: 10.1186/s41747-023-00344-x. View

18.

Mwikirize C, Nosher J, Hacihaliloglu I . Convolution neural networks for real-time needle detection and localization in 2D ultrasound. Int J Comput Assist Radiol Surg. 2018; 13(5):647-657. DOI: 10.1007/s11548-018-1721-y. View

19.

Defeudis A, Panic J, Nicoletti G, Mazzetti S, Giannini V, Regge D . Virtual biopsy in abdominal pathology: where do we stand?. BJR Open. 2023; 5(1):20220055. PMC: 10077420. DOI: 10.1259/bjro.20220055. View

20.

Lin Y, Paolucci I, Anderson B, OConnor C, Rigaud B, Briones-Dimayuga M . Study Protocol COVER-ALL: Clinical Impact of a Volumetric Image Method for Confirming Tumour Coverage with Ablation on Patients with Malignant Liver Lesions. Cardiovasc Intervent Radiol. 2022; 45(12):1860-1867. PMC: 9712233. DOI: 10.1007/s00270-022-03255-3. View