Intraoperative application of different imaging techniques in sacral neuromodulation: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Sacral neuromodulation (SNM) treats bladder dysfunction by implanting electrodes in the sacral foramen to regulate bladder reflexes. Accurate electrode placement is critical but challenging due to anatomical variations, intestinal gas interference, and radiation exposure from X-ray fluoroscopy. Alternative imaging methods are needed to improve precision and safety. METHODS: We searched PubMed, EMBASE, and Ovid Medline for studies (inception–June 2025) comparing imaging techniques for SNM. Outcomes included operative time, puncture attempts, and radiation dose. Study quality was assessed using ROB2, Newcastle‒Ottawa, and JBI tools. RESULTS: Sixteen studies were included. Beyond X-ray fluoroscopy, ultrasound, CT, 3D printing, O-arm, reduced C-arm fluoroscopy, and electromagnetic navigation were successfully applied. Ultrasound shortened procedure time, reduced punctures, and lowered radiation (3 studies). CT (5 studies), O-arm (2 studies), and computer-assisted lead placement (1 study) also proved effective. 3D printing decreased test time, puncture attempts, and radiation (5 studies). Reduced fluoroscopy minimized radiation while maintaining success. CONCLUSION: Ultrasound, CT, and 3D printing enhance SNM success by reducing fluoroscopy time, puncture attempts, and radiation exposure compared to conventional X-ray methods. Further large-scale randomized trials are needed to validate these techniques and explore multi-modal imaging fusion for improved outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".