Therapeutic developments in interventionist radiology from the viewpoint of artificial intelligence
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https://doi.org/10.21142/mecp.2026.1.1.e005Abstract
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1. Von Ende E, Ryan S, Crain MA, Makary MS. Artificial Intelligence, Augmented Reality, and Virtual Reality Advances and Applications in Interventional Radiology. Diagnostics (Basel). 2023;13(5):892. doi: 10.3390/diagnostics13050892.
2. Gurgitano M, Angileri SA, Rodà GM, Liguori A, Pandolfi M, Ierardi AM, et al. Interventional Radiology ex-machina: impact of Artificial Intelligence on practice. Radiol Medica. 2021;126(7):998–1006. doi: 10.1007/s11547-021-01351-x.
3. Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, et al. Predicting Treatment Response to Intraarterial 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. doi: 10.1016/j.jvir.2018.01.769.
4. Nielsen M, Waldmann M, Frölich AM, Flottmann F, Hristova E, Bendszus M, et al. Deep Learning-Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proofof-Principle Study. Stroke. 2021;52(11):3497-504. doi: 10.1161/ STROKEAHA.120.033807.
5. Bang JY, Hough M, Hawes RH, Varadarajulu S. Use of Artificial Intelligence to Reduce Radiation Exposure at FluoroscopyGuided Endoscopic Procedures. Am J Gastroenterol. 2020;115(4):555-61. doi: 10.14309/ajg.0000000000000565.
6. Yang S, Kweon J, Roh JH, Lee JH, Kang H, Park LJ, et al. Deep learning segmentation of major vessels in X-ray coronary angiography. Sci Rep. 2019;9(1):16897. doi: 10.1038/s41598-019- 53254-7.
7. 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. doi: 10.1186/s41747-023-00344-x.
8. Mehrtash A, Ghafoorian M, Pernelle G, Ziaei A, Heslinga FG, Tuncali K, et al. Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy. IEEE Trans Med Imaging. 2019;38(4):1026-36. doi: 10.1109/TMI.2018.2876796.
9. Choi JW, Cho YJ, Ha JY, Lee SB, Lee S, Choi YH, et al. Generating synthetic contrast enhancement from noncontrast chest computed tomography using a generative adversarial network. Sci Rep. 2021;11(1):20403. doi: 10.1038/ s41598-021-00058-3.
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