VALIDATION AND FEASIBILITY OF AN AUTOMATED SYSTEM FOR THE ASSESSMENT OF VASCULAR STRUCTURE AND MECHANICAL PROPERTIES IN THE DIGITAL ARTERIES THROUGH ULTRA-HIGH FREQUENCY ULTRASOUND
Bibliographic record
Abstract
Objective: The validation of a semi-automatic software to quantify vascular structure and mechanical properties of digital arteries acquired using ultra high frequency ultrasound (UHFUS). Design and method: UHFUS 5-second longitudinal scans of digital arteries of 15 patients with vascular diseases and 15 healthy controls were obtained by VevoMD (70 MHz probe, FUJIFILM, VisualSonics, Toronto, Canada) and analyzed using Carotid Studio (Quipu Srl, Pisa, Italy), using as reference technique a manual measurement in a Matlab interface (MathWorks, R2019b). Agreement between the two techniques for diameter, distension and intima-media thickness (IMT) was evaluated using Bland-Altman analyses; inter- and intra-operator reproducibility was carried out using coefficients of variation (CV). Results: No trend or significant bias were observed between Carotid Studio and Matlab manual analysis for diameter, distension, and IMT. All limits of agreement were acceptable. Intra-observer CV of diastolic diameter and IMT were 4.1 %, and 4.2 % respectively. Inter-observer CV for diastolic diameter, and IMT were 7.3 % and 5.4% respectively. Intra- and inter- observer CV for distension were higher (25.7 % and 26.7 % respectively). Conclusions: Carotid Studio software is a valid and reproducible tool for the assessment of vascular structure and mechanical properties in UHFUS scans of digital arteries.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".