Ultrahigh Frequency Ultrasound Imaging of the Hand: A New Diagnostic Tool for Hand Surgery
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it