Neural networks pipeline for quality management in IVF laboratory DOI Creative Commons

Sergei Sergeev,

Iuliia Diakova,

Lasha Nadirashvili

et al.

Journal of IVF-Worldwide, Journal Year: 2024, Volume and Issue: 2(4)

Published: Oct. 23, 2024

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both and laboratory data. We developed metamodel combining deep networks Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy interpretability. The achieved robust performance metrics after training fitting on 11500 cases: = 0.72, AUC 0.75, F1 score 0.60, Matthews Correlation Coefficient of 0.42. According morpho-kinetical embryo evaluation, our model’s PRC 0.66 significantly improves over existing time-lapse systems prediction, demonstrating better handling imbalanced metamodel’s calibration (Brier 0.20, expected error 0.06, maximum 0.12, Hosmer-Lemeshow test p-value 0.06) indicate reliability outcomes. validated the reproducibility using an independent dataset 665 treatment cycles, showing close alignment between predicted actual (58.9% vs. 59.1%). With Bayesian method, we proposed framework historical data with real-time predictions from enabling transition retrospective prospective analysis. Our approach extends beyond conventional selection, incorporating post-analytical phase evaluation laboratory. comprehensive enables detailed analysis across different patient subpopulations time periods, facilitating identification systemic issues protocol optimization. ability track probabilities staff members allows outcome prediction assessment efficacy, providing data-driven strategy continuous improvement assisted reproductive technology.

Language: Английский

Can Elective Single Embryo Transfer (eSET) with AI Integration Become the Future of IVF? DOI

Zeev Shoham

Journal of IVF-Worldwide, Journal Year: 2025, Volume and Issue: 3(1)

Published: Feb. 25, 2025

This manuscript examines whether elective single embryo transfer (eSET) should be mandated in all IVF cycles, assessing its clinical benefits, challenges, and global implementation. Evidence shows that eSET significantly reduces multiple pregnancies associated complications while maintaining cumulative live birth rates. However, ethical, regulatory, practical considerations complicate universal enforcement. While a standardized policy offers advantages, potential drawbacks include restricted patient autonomy the need for additional cycles some instances. To navigate these complexities, advocates balanced approach: promoting as preferred standard allowing individualized decisions based on patient-specific factors. Alternative models—such partial mandates or incentive-based strategies—are explored to enhance outcomes without imposing rigid requirements. nuanced perspective balances safety, treatment efficacy, shared decision-making fertility care.

Language: Английский

Citations

0
Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study DOI Creative Commons
Jingru Zhong, Ting Zhu, Yafang Huang

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e56774 - e56774

Published: Feb. 25, 2025

Background The surge in artificial intelligence (AI) interventions primary care trials lacks a study on reporting quality. Objective This aimed to systematically evaluate the quality of both published randomized controlled (RCTs) and protocols for RCTs that investigated AI care. Methods PubMed, Embase, Cochrane Library, MEDLINE, Web Science, CINAHL databases were searched until November 2024. Eligible studies or full exploring was assessed using CONSORT-AI (Consolidated Standards Reporting Trials–Artificial Intelligence) SPIRIT-AI (Standard Protocol Items: Recommendations Interventional checklists, focusing intervention–related items. Results A total 11,711 records identified. In total, 19 21 RCT 35 included. overall proportion adequately reported items 65% (172/266; 95% CI 59%-70%) 68% (214/315; 62%-73%) protocols, respectively. percentage specific item ranged from 11% (2/19) 100% (19/19) 10% (2/21) (21/21), exhibited similar characteristics trends. They lack transparency completeness, which can be summarized three aspects: without providing adequate information regarding input data, mentioning methods identifying analyzing performance errors, stating whether how intervention its code accessed. Conclusions could improved protocols. helps promote transparent complete with

Language: Английский

Citations

0
Neural networks pipeline for quality management in IVF laboratory DOI Creative Commons

Sergei Sergeev,

Iuliia Diakova,

Lasha Nadirashvili

et al.

Journal of IVF-Worldwide, Journal Year: 2024, Volume and Issue: 2(4)

Published: Oct. 23, 2024

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both and laboratory data. We developed metamodel combining deep networks Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy interpretability. The achieved robust performance metrics after training fitting on 11500 cases: = 0.72, AUC 0.75, F1 score 0.60, Matthews Correlation Coefficient of 0.42. According morpho-kinetical embryo evaluation, our model’s PRC 0.66 significantly improves over existing time-lapse systems prediction, demonstrating better handling imbalanced metamodel’s calibration (Brier 0.20, expected error 0.06, maximum 0.12, Hosmer-Lemeshow test p-value 0.06) indicate reliability outcomes. validated the reproducibility using an independent dataset 665 treatment cycles, showing close alignment between predicted actual (58.9% vs. 59.1%). With Bayesian method, we proposed framework historical data with real-time predictions from enabling transition retrospective prospective analysis. Our approach extends beyond conventional selection, incorporating post-analytical phase evaluation laboratory. comprehensive enables detailed analysis across different patient subpopulations time periods, facilitating identification systemic issues protocol optimization. ability track probabilities staff members allows outcome prediction assessment efficacy, providing data-driven strategy continuous improvement assisted reproductive technology.

Language: Английский

Citations

0