Suitability of antral follicle counts and computer-assisted analysis of ultrasonographic and magnetic resonance images for estimating follicular reserve in porcine, ovine and bovine ovaries <i>ex situ</i>
Bibliographic record
Abstract
This study was conducted to determine if correlations exist between the numbers of microscopic follicles comprising ovarian follicular reserve (OFR) and antral follicle counts (AFCs), and to assess the usefulness of computerized analyses of ovarian ultrasonograms and magnetic resonance (MR) images for estimating OFR in excised porcine, ovine and bovine ovaries. As a pre-requisite to these analyses, we characterized and compared ovarian cortical histomorphology and follicle populations in the three species varying in prolificacy and overall reproductive longevity, and hence the total number of microscopic and antral follicles. Ultrasonographic and MR images were obtained at the scanner settings optimized to provide opposing contrasts between antral follicles and the ovarian stroma. Commercially available ImageProPlus® analytical software was used to calculate numerical pixel values (NPVs) and pixel heterogeneity (standard deviation of the pixel values) along the computer-generated lines (4-6) placed in the area corresponding to the ovarian cortex. The numbers of primordial (r = 0.38, P < 0.01) and intermediate follicles (r = 0.37, P < 0.01) were correlated with the numbers of antral follicles in bovine ovarian sections. The numbers of primordial (r = 0.28, P < 0.05), intermediate (r = 0.31, P < 0.01) and primary follicles (r = 0.27, P < 0.05) correlated directly with mean NPVs of the ultrasonographic ovarian images in cattle. There was a negative correlation between primary follicle numbers and NPVs of MR images (3D FAST-SPOILED GRADIENT ECHO) of the porcine ovarian cortex (r = -0.31, P < 0.05). To summarize, the numbers of primordial and intermediate follicles could only be estimated from AFCs in cows. Using ultrasound NPVs, the numbers of primordial, intermediate and primary follicles could be directly estimated in bovine ovaries and the quantitative image attributes of MR images were useful for quantifying porcine primary follicles. The bovine ovarian model is compatible with human situation and hence future studies should be undertaken to ascertain the usefulness of AFCs and ultrasonographic image analyses for estimating OFR in women.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".