Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence.

Area of Science:

• Reproductive Medicine
• Artificial Intelligence in Healthcare

Background:

• Subfertility affects numerous couples worldwide, necessitating advanced reproductive technologies.
• In vitro fertilization (IVF) success rates can be improved through personalized treatment strategies.

Purpose of the Study:

• To develop and evaluate a predictive model for in vitro fertilization (IVF) outcomes.
• To assist clinicians in tailoring personalized treatment plans for subfertile couples.
• To enhance the overall success rates of assisted reproduction.

Main Methods:

• Construction of an enhanced web-based system utilizing a novel Artificial Neural Network (ANN) architecture.
• Integration of clinical and bibliographical standards for input and output parameters.
• Training of the ANN model by an expert in an IVF setting using a comprehensive dataset.

Main Results:

• The developed system functions as an information technology platform for IVF units.
• It rapidly and automatically recalls and evaluates extensive data.
• Provides objective predictions for artificial reproductive cycle outcomes.

Conclusions:

• Artificial Neural Networks (ANNs) are highly suitable for providing fertility specialists with numerical estimates.
• ANNs facilitate the personalization of healthcare in reproductive medicine.
• The model supports the adaptation of treatment courses based on specific patient indications.