Image-guided robotics for biopsy and ablation aims to minimize procedure times, reduce needle manipulations, radiation, and complications, and enable treatment of larger and more complex tumors, while facilitating standardization for more uniform and improved outcomes. Robotic navigation of needles enables standardized and uniform procedures which enhance reproducibility via real-time precision feedback, while avoiding radiation exposure to the operator. Robots can be integrated with computed tomography (CT), cone beam CT, magnetic resonance imaging, and ultrasound and through various techniques, including stereotaxy, table-mounted, floor-mounted, and patient-mounted robots. The history, challenges, solutions, and questions facing the field of interventional radiology (IR) and interventional oncology are reviewed, to enable responsible clinical adoption and value definition via ergonomics, workflows, business models, and outcome data. IR-integrated robotics is ready for broader adoption. The robots are coming!
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http://dx.doi.org/10.1055/s-0041-1739164 | DOI Listing |
Int J Gynecol Cancer
January 2025
Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.
J Imaging
January 2025
Clinic of Medicine, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway.
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a cornerstone in minimally invasive thoracic lymph node sampling. In lung cancer staging, precise assessment of lymph node position is crucial for clinical decision-making. This study aimed to demonstrate a new deep learning method to classify thoracic lymph nodes based on their anatomical location using EBUS images.
View Article and Find Full Text PDFJ Vasc Interv Radiol
January 2025
Department of Vascular and Interventional Radiology, Singapore General Hospital, Outram Rd, Singapore 169608, Singapore.
Purpose: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for CT-guided biopsy, compared to the conventional freehand technique.
Materials And Methods: Eight nodules within an abdominal phantom, incorporating the simulated vertebrae and ribs, were designated as targets. A robotic system was used for lesion detection, trajectory generation, and needle-holder positioning.
Thorax
January 2025
Division of Pulmonary, Allergy, and Sleep Medicine, Mayo Clinic Florida, Jacksonville, Florida, USA.
Background: Sampling of peripheral pulmonary lesions (PPLs) abutting the pleura carries a higher risk of pneumothorax and complications. Although typically performed with image-guided transthoracic biopsy, the advent of shape-sensing robotic-assisted bronchoscopy (ssRAB) provides an alternative diagnostic procedure for this subtype of lesions.
Methods: A retrospective study on PPL attached to the peripheral pleura (PP), comprising costal and diaphragmatic pleura, mediastinal pleura (MP), and fissural pleura (FP) sampled by ssRAB, from January 2020 to December 2023.
Tech Vasc Interv Radiol
December 2024
Department of Interventional Radiology, MedStar Georgetown University Hospital, Washington, DC. Electronic address:
Artificial intelligence and robotics are transforming interventional radiology, driven by advancements in computer vision, robotics and procedural automation. Historically focused on diagnostics, AI now also enhances procedural capabilities in IR, enabling future robotic systems to handle complex tasks such as catheter manipulation or needle placement with increasing precision and reliability. Early robotic systems in IR demonstrated improved accuracy in both vascular and percutaneous interventions, though none were equipped with automatic decision-making.
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