Clinical applications of digital twin technology in In Vitro Fertilisation

Area of Science:

• Reproductive Medicine
• Biomedical Engineering
• Artificial Intelligence in Healthcare

Background:

• In vitro fertilisation (IVF) faces challenges in embryo selection, outcome prediction, and treatment personalization.
• Existing assisted reproductive technology research is fragmented, lacking integrated frameworks.
• Digital twin technology offers potential for virtual replicas, real-time monitoring, and predictive modeling in healthcare.

Purpose of the Study:

• To review current digital twin applications in IVF.
• To evaluate the benefits and limitations of digital twin technology in IVF.
• To propose an integrative conceptual model and identify future research directions for digital twins in reproductive medicine.

Main Methods:

• A comprehensive narrative review of literature from 2015-2025 was conducted.
• Databases searched included PubMed, Scopus, Web of Science, and IEEE Xplore.
• Search terms focused on digital twins, IVF, and assisted reproductive technology, including proof-of-concept studies.

Main Results:

• Digital twins show potential in IVF for embryo development simulation, ovarian response prediction, and personalized protocols.
• Current applications integrate AI, machine learning, time-lapse imaging, and omics data.
• Early evidence indicates improved embryo selection and treatment optimization, but large trials are limited; challenges include data integration and validation.

Conclusions:

• Digital twin technology signifies a paradigm shift towards personalized, predictive, and precision medicine in IVF.
• An integrative conceptual model is proposed for digital twin implementation across the IVF spectrum.
• Addressing knowledge gaps and research priorities is crucial for clinical translation and improved IVF outcomes.