Advancements in needle visualization enhancement and localization methods in ultrasound: a literature review
Bibliographic record
Abstract
Ultrasound guidance plays a central role in numerous minimally invasive procedures involving percutaneous needle insertion, ensuring safe and accurate needle placement. However, it encounters two primary challenges: (1) aligning the needle with the ultrasound beam and (2) visualizing the needle even when correctly aligned. In this review, we offer a concise overview of the physics foundation underlying these challenges and explore various approaches addressing specific challenges, with a focus on software-based solutions. We further distinguish between hardware-based and software-based solutions, placing a stronger emphasis on the latter. The incorporation of artificial intelligence into these methods to enhance needle visualization and localization is briefly discussed. We identify state-of-the-art needle detection methods, showcasing submillimeter precision in tip localization and orientation. Additionally, we provide insights into potential future directions, aiming to facilitate the translation of these advanced methods into the clinic. This article serves as a comprehensive guide, offering insights into challenges, evolving solutions, and prospective research directions to effectively address these issues.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".