P–016 Using artificial intelligence to predict semen upgrading after microsurgical varicocele repair
Bibliographic record
Abstract
Abstract Study question Can we use artificial intelligence models to predict semen upgrading after microsurgical varicocele repair? Summary answer A machine learning model performed well in predicting clinically meaningful post-varicocelectomy semen upgrade using pre-operative hormonal, clinical, and semen analysis data. What is known already Varicocele repair is recommended in the presence of a clinical varicocele together with at least one abnormal semen parameter, and male infertility. Unfortunately, up to 50% of men who meet criteria for repair will not see meaningful benefit in outcomes despite successful surgery. Nomograms exist to help predict success, but these are based out of single-center databases, do not incorporate hormonal data, and are rarely designed to predict pre-defined, clinically meaningful improvements in semen parameters. Study design, size, duration Data were collected from an international, multi-center retrospective cohort. A total of 240 men were identified. Data from 160 men from Miami, USA and 80 men from Toronto, Canada were included. Data was collected from 2006 to 2020. Participants/materials, setting, methods We collected pre and postoperative clinical data following varicocele surgery. Clinical upgrading was defined as an increase in sperm concentration that would allow a couple to access new reproductive technologies/techniques. The tiers used for upgrading were 0–1million/cc (Intracytoplasmic Sperm Injection), 1–5 million (In Vitro Fertilization), 5–15 million (Intrauterine Insemination), and >15 million (Natural conception). Artificial intelligence models were trained and tested using R to predict which patients upgraded after surgery. Main results and the role of chance 51% of men underwent bilateral varicocele repair. The majority of men had grade 2 varicocele on the left, and (when present) a grade 1 varicocele on the right. Overall, 47% of men experienced an upgrade following varicocele surgery, 47% did not change, and 6% downgraded. The data from Miami were used to create a random forest model for predicting clinically significant upgrade in sperm concentration. The most informative model parameters were preoperative FSH, sperm concentration, and surgical laterality. The model identified three clinical categories: men with unfavorable, intermediate, and favorable features to predict varicocele upgrade. On external validation using data from Toronto, the model accurately predicted upgrade in 87% of men with favorable features, and in 49% and 36% of men with intermediate and unfavorable features, respectively. Overall, the model performed well on external validation with an AUC of 0.72 and good calibration. Calibration plots, using cross-validation, define how well the predicted probabilities match the actual probability of sperm concentration upgrade. The random forest model was run twelve times. All model characteristics are the mean of ten model runs with the highest and lowest performing runs removed. The model was translated to an online calculator that can be used by clinicians. Limitations, reasons for caution One limitation to our study is that we were not able to predict total motile sperm count (TMSC), which has been shown to perform slightly better than concentration at predicting assisted reproduction outcomes. By focusing on clinically significant upgrading, this difference should be minimized. Wider implications of the findings: Predicting the chances of clinically significant semen upgrading after varicocele repair is essential for patients and clinicians to understand. Several men undergo surgery with no subsequent benefit, which may lead to a delay in definitive treatment with IVF/IUI. Understanding their chances will help couples make better informed decisions moving forward. Trial registration number Not applicable
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".