Artificial intelligence in IVF: a need.

Area of Science:

• Reproductive Medicine
• Artificial Intelligence
• Biostatistics

Background:

• Predicting in-vitro fertilization (IVF) outcomes remains challenging due to center-specific result variations.
• Accurate prediction systems are essential for improving success rates in assisted reproductive technologies.
• Artificial neural networks (ANNs) offer adaptive learning and prediction capabilities relevant to complex medical data.

Purpose of the Study:

• To review existing artificial neural network (ANN) and statistical models for predicting IVF outcomes.
• To identify key input parameters utilized in previous IVF prediction studies.
• To propose and evaluate a novel ANN architecture for enhanced IVF outcome prediction.

Main Methods:

• Comprehensive literature review of ANNs, ensemble techniques, CART, regression analysis, discriminant analysis, and case-based reasoning for IVF prediction.
• Analysis of input parameters employed in various IVF outcome prediction models.
• Development and evaluation of a new ANN architecture using the Learning Vector Quantizer (LVQ) on a complete IVF dataset.

Main Results:

• Past research extensively explored various statistical and machine learning models for IVF prediction.
• Commonly used input parameters include patient demographics, clinical history, and cycle-specific variables.
• The proposed LVQ-based ANN architecture demonstrates potential for good generalization and predictive power.

Conclusions:

• ANNs show significant promise in enhancing the predictability of IVF treatment outcomes.
• Further development and validation of advanced ANN architectures are warranted.
• Accurate IVF outcome prediction can aid in personalized treatment strategies and resource allocation.