Urology
October 16, 2026 · via Europe PMC
Feasibility of AI-based Automated Identification of Sacral Anatomy and Lead Features During Sacral Neuromodulation Procedures.
Hugenberg NR, Khanna A, Linder BJ.
AI summary — for doctors, generated from the abstract only
AI segmentation of sacral anatomy and electrodes on fluoroscopy during sacral neuromodulation is feasible, pending further validation.
What was studied
Expert-segmented fluoroscopic image pairs from device placements (November 2017 to August 2022) were used to train and test nnUNet networks for anatomy (lateral train 177, test 43; AP train 170, test 36) and electrodes (train 201, test 38).
Key findings
- Experts judged segmentation acceptable in 97% (35/36) of AP and 93% (40/43) of lateral images
- Mean Dice score was 0.759 for lateral and 0.711 for AP landmarks
- Electrode Dice score was 0.889
Clinical relevance
Automated landmark and lead identification could eventually guide or standardise lead placement.
Who should care
Limitations
Small test sets, single-source data, no clinical outcomes, and some landmarks scored lower (S2 Dice 0.524); the authors state further validation is required.
A proof of feasibility, not yet ready for clinical use.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objective
To evaluate the ability of artificial intelligence-based modeling to identify pertinent sacral anatomy and lead characteristics on fluoroscopic imaging.
Methods
An expert practitioner manually reviewed and segmented image pairs from sacral neuromodulation device placements between November 2017 and August 2022. These were used to train (n=177 lateral, 170 AP) and test (n=43 lateral, 36 AP) two semantic segmentation networks via nnUNet. A subset of this data was selected to train a model for electrode segmentation (n=201 train, n=38 test). Dice scores and subjective review were used to assess correlation between expert annotation and automated AI segmentation.
Results
The AI algorithm accurately segmented pertinent anatomic landmarks in 97% (35/36) of AP images and 93% (40/43) of lateral images based on subjective binary expert review, indicating a high level of clinically acceptable segmentations. When evaluated by 3-point Likert scale, the mean (SD) score was 2.86 (0.42) for AP images and 2.77 (0.57) for lateral images. The overall Dice score for all lateral landmarks was (mean ± std) 0.759 ± 0.067, with individual landmark scores ranging from 0.524 (S2) to 0.769 (S3 hillock). The overall Dice score (integrated) for all AP landmarks was 0.711 ± 0.117, with individual landmark scores ranging from 0.633 (medial edge of foramen) to 0.745 (midline of spine). The overall Dice score (integrated) for all electrodes in the combined AP and lateral imaging dataset was 0.889 ± 0.035.
Conclusions
Creating segmentation networks capable of producing high-quality annotations of the pertinent sacral anatomy and leads during sacral neuromodulation device placement is feasible. Further validation is needed prior to clinical implementation.
DOI
10.1016/j.urology.2026.10.011
PMID
42887826
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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