Ultrasound-based geometric modeling of the human ovary with applications to cryopreservation
Bibliographic record
Abstract
Successful cryopreservation of the whole ovary outside of the body, while a woman undergoes cancer treatments, may help preserving fertility and regaining hormone balance during recovery. One of the key challenges in whole ovary cryopreservation is adequately loading the organ with cryoprotective agents (CPAs). Another notable challenge in developing the application is the lack of geometric data needed for designing matching thermal protocols. The objective of the current study is twofold: (i) to develop an effective geometric reconstruction method for the ovary, based on transvaginal ultrasound (TVUS) data, and (ii) to perform a pilot study on the thermal effects associated with CPA loading with application to vitrification. This study includes screening of 127 TVUS imaging datasets of ovaries from healthy ovulatory participants, reconstruction of 14 geometric models, and thermally analyzing two representative geometric models of low and high mature follicles-to-organ volume ratios. Results of this study demonstrate that the proposed reconstruction method is faster and more accurate than that facilitated by commercially available software (SonoAVC, GE Healthcare). Two extremes were investigated: (1) complete vitrification of the ovary, and (2) crystallization of mature follicles while the remaining ovarian stroma vitrifies. CPA loading into the mature follicles is considered an outstanding cryopreservation challenge, but with very little impact on long-term fertility preservation. Results of this study suggest that ovarian preservation by vitrification is feasible when sufficient CPA loading is achieved, while identifying the most suitable CPA for the task remains a challenge beyond the scope of the current study.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".