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Purpose Of Review: Minimally invasive spine surgery (MIS) and robotic technology are growing in popularity and are increasing utilized in combination. The purpose of this review is to identify the current successes, potential drawbacks, and future directions of robotic guidance for MIS compared to traditional techniques.
Recent Findings: Recent literature highlights successful incorporation of robotic guidance in MIS as a consistently accurate method for pedicle screw placement. With a short learning curve and low complication rates, robot guidance may also reduce the use of fluoroscopy, operative time, and length of hospital stay. Recent literature suggests that incorporating robotic guidance in MIS improves the accuracy of pedicle screw insertion and may have added benefits both intra- and postoperatively for the patient and provider. Future research should focus on direct comparison between MIS with and without robotic guidance.
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http://dx.doi.org/10.1007/s12178-019-09558-2 | DOI Listing |
Med Image Anal
August 2025
The Chinese University of Hong Kong, 999077, Hong Kong Special Administrative Region of China. Electronic address:
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making. In the context of robotic-assisted surgery, MLLMs can serve as effective tools for surgical training and guidance. However, there is still a deficiency of MLLMs specialized for surgical scene understanding in endoscopic procedures.
View Article and Find Full Text PDFInterdiscip Cardiovasc Thorac Surg
September 2025
Department of Electrophysiology, Abbott Inc, Chicago, IL.
We report the first use of the EnSite X system for intraoperative electrophysiological mapping during a robotic hybrid ablation (ROK-AF procedure) for long-standing persistent atrial fibrillation. Epicardial ablation targets were identified, and post-ablation electrical silencing was validated. Unlike conventional systems, its orientation-independent omnipolar technology provides directional activation vectors, high-resolution electrograms, and peak frequency analysis, thereby enhancing substrate characterisation.
View Article and Find Full Text PDFILIVER
September 2025
Division of Hepatobiliary and Transplantation Surgery, Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China.
Anatomic resection remains a fundamental principle in the surgical management of hepatobiliary diseases, whether performed through traditional open surgery or advanced minimally invasive approaches such as laparoscopic or robotic-assisted techniques. However, a universally accepted and clearly defined anatomical framework for intraoperative anatomical delineation remains lacking. The growing clinical adoption of Laennec membrane-guided anatomical strategies has been associated with notable improvements in surgical efficacy and anatomical precision.
View Article and Find Full Text PDFMed Biol Eng Comput
September 2025
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China.
Surgical instrument segmentation plays an important role in robotic autonomous surgical navigation systems as it can accurately locate surgical instruments and estimate their posture, which helps surgeons understand the position and orientation of the instruments. However, there are still some problems affecting segmentation accuracy, like insufficient attention to the edges and center of surgical instruments, insufficient usage of low-level feature details, etc. To address these issues, a lightweight network for surgical instrument segmentation in gastrointestinal (GI) endoscopy (GESur_Net) is proposed.
View Article and Find Full Text PDFNeural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDF