Enhancing Peripheral Nerve Blocks: Real-Time Ultrasound Guidance for Nerve Detection and Needle Placement
Bibliographic record
Abstract
Ultrasound-guided nerve blocks are an essential element of regional anesthesia, providing effective localized pain relief while minimizing the risks associated with systemic medications. However, their adoption remains limited by procedural complexity, reliance on practitioner expertise, and the inherent challenges of ultrasound image interpretation. This study introduces an AI-assisted platform that integrates simultaneous real-time detection of the nerve and needle for the transversus abdominis plane block, addressing a critical gap in the literature. Utilizing a dataset of annotated ultrasound images, the authors developed and trained segmentation models based on the R2U-Net architecture. The nerve detection model achieved a Dice score of 0.8384, while the needle detection model reached a Dice score of 0.8309. These models were integrated into an Android-based application, offering real-time visualization and intuitive controls with low latency (<200 milliseconds) and consistent frame rates (24 FPS). The system has the potential to improve procedural precision, reduce variability across practitioners, and expand access to nerve blocks for non-specialists. Future work will focus on expanding clinical datasets, conducting validation studies, and exploring model enhancements to further refine its applicability.Clinical Relevance- This system serves as a valuable companion tool in ultrasound-guided nerve blocks, providing real-time visualization and feedback to support clinicians, particularly those with less experience. By enhancing procedural guidance, it facilitates teaching and improves confidence, enabling a broader range of practitioners to perform nerve blocks effectively and safely.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".