SynthesisJournal of robotic surgery2026
A standardized workflow for precision liver resection: systematic review of integrated 3D visualization, indocyanine green fluorescence, and augmented reality navigation.
Synthesis in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital technologies (3D imaging, surgical navigation, AR, and robot-assisted hepatobiliary technologies) have advanced liver surgery significantly. This systematic review synthesizes 2010–2025 evidence to propose a standardized workflow integrating preoperative 3D modeling, ICG fluorescence imaging, AR navigation, and (where applicable) robot-assisted techniques. We searched PubMed, Embase, Cochrane Library, and Web of Science (Jan 2010–Jul 2025) using keywords/MeSH terms. Included studies were English-language clinical studies (RCTs, cohort studies, case-control studies, case series ≥ 10 patients) on these technologies for liver resection (benign/malignant tumors) with extractable outcome data. Reviews, editorials, conference abstracts, and animal/basic research were excluded. Data on R0 resection rates, complications, and technical innovations were extracted. Bias risk was assessed via ROBINS-I (non-randomized studies) and Cochrane Tool 2.0 (RCTs if included); two reviewers worked independently, with disagreements resolved by consensus or a third senior reviewer. Narrative synthesis was used due to clinical heterogeneity. Of 1247 records, 42 met inclusion criteria. The integrated approach (3D modeling + ICG + AR ± robot-assisted technologies) showed potential for higher R0 resection rates compared to conventional techniques in several cohort studies. According to HCC management guidelines, a > 15% improvement in R0 resection rates is considered clinically meaningful, suggesting the potential clinical value of these techniques. Key innovations included an ICG dosage algorithm (0.25 mg/kg + 0.01 mg/cm³ tumor volume) and standardized AR-guided pedicle dissection. Per HCC guidelines, > 15% R0 improvement is clinically meaningful, highlighting the techniques’ value. This systematic review addresses this gap by synthesizing evidence from 2010 to 2025 to answer the following question: In patients undergoing liver resection, what is the evidence for an integrated workflow combining preoperative 3D modeling, intraoperative ICG fluorescence imaging, and augmented reality navigation in improving surgical precision and outcomes? Our primary objective is to evaluate the feasibility, clinical outcomes, and key technical components of this integrated approach to propose a standardized, evidence-based workflow for clinical practice.
Indexed as
Identifiers
41933224What 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.