Ultrahigh Frequency Ultrasound Imaging of the Hand: A New Diagnostic Tool for Hand Surgery
Bibliographic record
Abstract
BACKGROUND: Ultrasonography is a cost-effective, noninvasive, and expedient imaging modality with numerous clinical applications. Conventional ultrasound uses transducers with frequencies that range from 5 to 12 MHz. However, ultrahigh frequency ultrasound (UHFUS) is capable of producing frequencies up to 70 MHz, which can achieve tissue resolution up to 30 μm. The purpose of our study is to present the capabilities of a novel technology and to describe its possible clinical applications for hand surgery. METHODS: The Vevo 2100 (VisualSonics, Toronto, Canada) system was used to perform all ultrasound exams. Four unique linear array transducers were employed. All studies were performed by the authors, who have no formal training in ultrasound techniques, on 5 healthy resident volunteers and 1 clinical patient under institutional review board approval. RESULTS: A series of 10 static images per participant and dynamic, real-time videos were obtained at various locations within the hand and wrist. UHFUS is capable of quickly and reliably imaging larger structures such as foreign bodies, soft tissue masses, and the flexor tendons, and diagnosing an array of pathologies within these structures. In addition, UHFUS can identify much finer structures such as the intimal layer of the arteries in the hand and individual fascicles within the digital nerves to provide data about vessel quality and vascular and neural pathologies. CONCLUSIONS: UHFUS is a novel technology that shows multiple advantages over conventional ultrasound for imaging the fine superficial structures of the hand and wrist, and can be deployed by the surgeon at the point of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".