Robotic systems in interventional oncology: a narrative review of the current status

Matsui, Yusuke; Kamegawa, Tetsushi; Tomita, Koji; Uka, Mayu; Umakoshi, Noriyuki; Kawabata, Takahiro; Munetomo, Kazuaki; Iguchi, Toshihiro; Matsuno, Takayuki; Hiraki, Takao

doi:10.1007/s10147-023-02344-8

Cited by 10 publications

(1 citation statement)

References 65 publications

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“…The disadvantage of this technique is the high level of radiation exposure that both the patient and the doctor who performs the procedure using the "free hand" method. Therefore, currently, robotic devices are being developed all over the world that allows interventional procedures to be performed without the direct participation of a doctor and reduce the radiation exposure on the patient [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Core-Biopsy of Mediastinal Neoplasms Using a Robotic Device Controlled by Computer Tomography

Pustovgar,

Yarosh,

Oleynik

et al. 2024

Core-Biopsy of Mediastinal Neoplasms Using a Robotic Device Controlled by Computer Tomography

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Section: Introductionmentioning

confidence: 99%

Core-Biopsy of Mediastinal Neoplasms Using a Robotic Device Controlled by Computer Tomography

Pustovgar,

Yarosh,

Oleynik

et al. 2024

Core-Biopsy of Mediastinal Neoplasms Using a Robotic Device Controlled by Computer Tomography

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A Hybrid Workspace Mapping Method Based on Force Feedback for Underwater Teleoperation Systems

Yang,

Gao,

Zhang

et al. 2023

Lecture Notes in Computer Science

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Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Matsui,

Ueda,

Fujita

et al. 2024

Jpn J Radiol

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Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

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Robotic systems in interventional oncology: a narrative review of the current status

Cited by 10 publications

References 65 publications

Core-Biopsy of Mediastinal Neoplasms Using a Robotic Device Controlled by Computer Tomography

Core-Biopsy of Mediastinal Neoplasms Using a Robotic Device Controlled by Computer Tomography

A Hybrid Workspace Mapping Method Based on Force Feedback for Underwater Teleoperation Systems

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

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