Comparison of automated and manual follicle monitoring in an unrestricted population of 100 women undergoing controlled ovarian stimulation for IVF
Bibliographic record
Abstract
BACKGROUND: Ovarian response to gonadotrophin stimulation is monitored with serial ultrasound (US) examinations. Sonography-based Automated Volume Count (SonoAVC) is a relatively new three-dimensional (3D) US technology, which automatically generates a set of measurements including the mean follicular diameter (MFD) and a volume-based diameter (d(V)) for each follicle in the ovaries. The present study aimed to assess the applicability and reproducibility of this automated follicle measurement method in an IVF programme. METHODS: For this prospective method comparison study, 100 women undergoing US monitoring of a controlled ovarian stimulation cycle were recruited. Each follicle was manually measured by taking the mean of maximal diameters on three orthogonal planes with two-dimensional (2D) US. A 3D volume of each ovary was then captured. The ovarian volumes were later analysed using SonoAVC. The agreement between the two methods for the numbers of follicles and the size of the leading follicle was assessed with the Bland-Altman method. The reproducibility of SonoAVC measurements was assessed with the intraclass correlation coefficient (ICC). RESULTS: Both SonoAVC-generated MFD and d(V)-based follicle counts, as well as the leading follicle diameter, had good agreement with conventional 2D US measurements. SonoAVC measurements had very good reproducibility, with ICC ≥0.8 for most evaluations. CONCLUSIONS: Automated follicle monitoring with SonoAVC can replace or be used interchangeably with conventional 2D measurements. Automated follicle monitoring can save time, provide a method of quality control and create opportunities for developing HCG criteria based on follicular volume or for monitoring patients from a distance.
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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 | 0.009 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".