3D ultrasound to investigate synovial blood flow in 1st carpometacarpal osteoarthritis
Bibliographic record
Abstract
The first carpometacarpal (CMC-1) joint is a common site of osteoarthritis (OA). The joint disease commonly presents with inflammation of the synovial membrane, synovitis. Inflammation and the formation of new blood vessels, angiogenesis, are integrated processes. Increased blood flow, angiogenesis and inflammation of the synovial tissue can contribute to symptoms of OA. The role angiogenesis plays in pathogenesis and disease progression is not fully understood. Imaging modalities, such as power Doppler (PD) ultrasound (US) can detect blood flow. Recently, a new Doppler ultrasound technique, superb microvascular imaging (SMI), was developed and uses an algorithm that can more effectively visualize low-velocity blood flow. To better understand the role of angiogenesis in OA and to visualize the three-dimensional (3D) vasculature, we developed a 3DUS system. This paper is a preliminary study, which demonstrates our 3DUS system acquiring PD and SMI images for CMC-1 OA to provide quantification as well as improved blood flow visualization. As part of a clinical trial, a patient presenting with CMC-1 OA was imaged using 3DUS PD and SMI technologies to quantify the synovial volume and Doppler signals. We found synovial Doppler signals present in 3D PD and SMI images. To optimize the temperature of the device scanning solution, healthy volunteers were imaged at increasing temperatures. The Doppler signals in the blood vessels were quantified and we observed an increase in Doppler signal with higher temperatures. This work demonstrates the ability of the 3DUS PD and SMI system to detect, quantify, and visualize vessel and synovial blood flow.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".