Validation of a New 3D-US Imaging Robotic System to Detect and Quantify Lower Limb Arterial Stenoses
Bibliographic record
Abstract
Stenosis degree is the most common criterion used to assess the severity of atherosclerosis. This form of peripheral arterial disease (PAD) is often present in lower limb arteries. However, to detect and quantify distributed arterial stenoses in lower limbs, a high precision is required over a long segment. Moreover, to plan the appropriate therapy, a 3D representation of the vessel is desirable. Most 3D-ultrasound (US) developments are not optimally adapted for this application. A new 3D-US imaging robotic system that can control and standardize the 3D-US acquisition process for any scanning distance is presented. A calibration study is performed to determine the spatial transform to relate the US probe image plane attached to the robotic system, to the robot coordinates. Additionally, 3D-US reconstructions of in-vitro stenoses were obtained with the robotic scanner and the spatial calibration transform computed. Thereafter, stenoses were detected and quantified from the 3D reconstructed model. Altogether, these results demonstrate the potential of the robot for the clinical evaluation of lower limb vessels over long and tortuous segments starting from the iliac artery down to the popliteal artery below the knee.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".