Innovations in assisted reproductive technology through the advances in artificial intelligence and image processing

Area of Science:

• Reproductive Medicine
• Biomedical Imaging
• Artificial Intelligence

Background:

• Traditional assisted reproductive technology (ART) methods for embryo selection and ovarian monitoring rely on subjective interpretation, leading to variability.
• Artificial intelligence (AI) offers enhanced objectivity, consistency, and reproducibility in clinical decisions within reproductive medicine.

Purpose of the Study:

• To explore the integration of AI and advanced imaging technologies in reproductive medicine.
• To highlight the potential of AI and fluorescence lifetime imaging microscopy for objective embryo and follicle evaluation.
• To discuss the application of AI in self-operated reproductive health monitoring.

Main Methods:

• Utilizing AI-enhanced models for consistent performance in embryo selection and monitoring.
• Employing fluorescence lifetime imaging microscopy for real-time, label-free metabolic assessment of gametes and embryos.
• Developing AI-powered self-assessment tools for ovarian follicles using smartphone-compatible ultrasound devices.

Main Results:

• AI models demonstrate reduced inter- and intra-observer variability compared to traditional methods.
• Metabolic signatures from fluorescence lifetime imaging correlate with embryo developmental competence, offering non-invasive quality assessment.
• AI facilitates automated follicle segmentation and self-assessment, improving ovarian stimulation tracking and patient engagement.

Conclusions:

• The integration of AI, advanced image processing, and metabolic imaging represents a significant advancement in reproductive medicine.
• These technologies enhance the precision of embryo and follicle evaluation.
• Multimodal platforms combining clinical, morphological, and metabolic data hold promise for optimizing ART outcomes.