Evidence map›Paper›PMID 41096028›Full record

ArticleJournal of clinical medicine2025

Personal KPIs in IVF Laboratory: Are They Measurable or Distortable? A Case Study Using AI-Based Benchmarking.

Péter Mauchart, Emese Wágner, Krisztina Gödöny, Kálmán Kovács, Sándor Péntek, Andrea Barabás, József Bódis, Ákos Várnagy

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Article in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Péter MauchartNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.
Emese WágnerDepartment of Obstetrics and Gynecology, Medical School, University of Pécs, H-7624 Pécs, Hungary.
Krisztina GödönyNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.
Kálmán KovácsNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.
Sándor PéntekNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.
Andrea BarabásNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.
József BódisNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.
Ákos VárnagyNational Laboratory on Human Reproduction, University of Pécs, H-7624 Pécs, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundKey performance indicators (KPIs) are widely used to evaluate embryologist performance in IVF laboratories, yet they are sensitive to patient demographics, treatment indications, and case allocation. Artificial intelligence (AI) offers opportunities to benchmark personal KPIs against context-aware expectations. This study evaluated whether personal CPR-based KPIs are measurable or distorted when compared with AI-derived predictions.

methodsWe retrospectively analyzed 474 ICSI-only cycles performed by a single senior embryologist between 2022 and 2024. A Random Forest trained on 1294 institutional cycles generated AI-predicted clinical pregnancy rates (CPRs). Observed and predicted CPRs were compared across age groups, BMI categories, and physicians using cycle-level paired comparisons and a grouped calibration statistic.

resultsOverall CPRs were similar between observed and predicted outcomes (0.31 vs. 0.33,

conclusionsPersonal embryologist KPIs are measurable but influenced by patient and physician factors. AI benchmarking may improve fairness by adjusting for case mix, yet systematic bias can persist in high-risk subgroups. Multi-operator, multi-center validation is needed to confirm generalizability.

Indexed as

IVF benchmarkingmachine learningoutcome predictionquality assessment

Identifiers

PMID41096028
PMCPMC12525357

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