ArticleInternational journal of computer assisted radiology and surgery2025
Semi-automatic puncture robotic system based on real-time multi-modal image fusion: preclinical evaluation.
Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- [Ultrasound technology's new application in the diagnosis and treatment of liver cancer].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- [Application and prospects of artificial intelligence from tool empowerment to paradigm reconstruction for whole-course diagnosis and treatment of liver diseases by ultrasound].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Feasibility, safety and accuracy of a novel robotic navigation system for cone-beam CT-guided and TACE-assisted radiofrequency ablation of hepatocellular carcinoma, a preliminary study.BMC gastroenterology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTraditional surgical robot system relying on computed tomography (CT) navigation suffers from two drawbacks during abdominal organ puncture surgeries. Firstly, the puncture target is displaced under the influence of respiration, thereby reducing the puncture accuracy. Secondly, the puncture process lacks real-time visualization, which may potentially give rise to medical accidents. This paper presents a semi-automatic surgical robot system based on the fusion guidance of real-time ultrasound images and CT, along with the monitoring of the patient's respiratory state, to address these issues.
methodThis system utilizes a six-axis force sensor in contact with the human body, and a respiratory model can be constructed through data got from force sensor to monitor the patient's respiratory phase and recommend the optimal puncture phase. The issue of non-real-time puncture guidance is addressed through the real-time registration and fusion of ultrasound (US) images with preoperative CT images.
resultsPhantom experiments and animal experiments were carried out based on this design. The test results indicate that in these two experiments, the average fusion error between US and CT of the main tissues in the liver is within 3 mm. For puncture accuracy, in the phantom experiment, the average puncture error was 1.0 mm, with a minimum of 0 mm and a maximum of 2.1 mm. In the animal experiment, the average puncture error was 2.5 mm, ranging from a minimum of 1.6 mm to a maximum of 3.0 mm.
conclusionThe results of two experiments show both the image fusion accuracy and puncture accuracy of this system are within 3mm, which can meet the requirement of 5 mm puncture accuracy in clinical practice. Approximately 70% of the operation are automatically accomplished by robot system, greatly reducing the reliance on the doctor's experience.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.