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Machine Learning Embryo Assessment—Implementation Before Validation Concern

May 18, 2026

Code to Conception

Daily micro-protocols for the 90-day miracle window

| May 18, 2026 |

🔬 Pre-Bump Biology  

AI doesn’t change your embryos—it simply reshuffles them. Machine-learning tools read tiny morphokinetic signals (cleavage timing, mitochondrial stress, fragmentation patterns) that reflect the biology you built in the 90-day window. The healthier your egg and sperm inputs now, the clearer the signal—and the higher the odds the top-ranked embryo truly is your best shot.

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🧬 Protocol Drop  

Today’s 1-Step Protocol: 

Ask your clinic one direct question: “Do you use AI to rank embryos only, or to discard them?” 

If discard is part of the workflow, request ranking-only. Current evidence (including the 2024 Nature Medicine RCT) shows AI can help organize embryos but should not be used to exclude embryos with live-birth potential. This protects your usable cohort—and your cumulative chance of success.

📚 Glossary Pop  

Morphokinetics: 

The precise timing of embryo cell divisions—when it reaches 2 cells, 3 cells, 5 cells, when it compacts, and when it becomes a blastocyst. Healthy embryos tend to follow smooth, predictable timelines. AI models analyze thousands of these tiny time-points to estimate which embryo is most likely to implant.

Send this to your partner so you both know the right question to ask at your next clinic visit. One sentence can protect multiple future transfers.
P.S. Tomorrow Teaser
Are IVF labs rolling out embryo-scoring AI faster than science can validate it? Tomorrow we pull back the curtain on the quiet trend shaping modern IVF—algorithms deployed before they’re proven, clinics adopting tools without clinic-level validation, and couples making decisions guided by scores that may not predict live birth. We’re decoding the real risks of “implement now, prove later” in machine-learning embryo assessment.

Want to learn more?

ESHRE Working group on Time-lapse technology, Apter, S., Ebner, T., Freour, T., Guns, Y., Kovacic, B., Le Clef, N., Marques, M., Meseguer, M., Montjean, D., Sfontouris, I., Sturmey, R., & Coticchio, G. (2020). Good practice recommendations for the use of time-lapse technology†. Human reproduction open, 2020(2), hoaa008. https://doi.org/10.1093/hropen/hoaa008

Illingworth, P. J., Venetis, C., Gardner, D. K., Nelson, S. M., Berntsen, J., Larman, M. G., Agresta, F., Ahitan, S., Ahlström, A., Cattrall, F., Cooke, S., Demmers, K., Gabrielsen, A., Hindkjær, J., Kelley, R. L., Knight, C., Lee, L., Lahoud, R., Mangat, M., Park, H., Price, A., Trew, G., Troest, B., Vincent, A., Wennerström, S., Zujovic, L., & Hardarson, T. (2024) Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind non-inferiority trial. Nature Medicine, 30, 3114-3120. https://doi.org/10.1038/s41591-024-03166-5 Nature

Mina, A., Younesi, M., Doohandeh, T., Darzi, S., Ajabi Ardehjani, N., Sheibani, S., Hosseinirad, H., & Valizadeh, R. (2025). Predicting pregnancy outcomes in IVF cycles: A systematic review and diagnostic meta-analysis of artificial intelligence in embryo assessment. Contraception & Reproductive Medicine, 10, Article 59. https://doi.org/10.1186/s40834-025-00400-4 BioMed Central+1

Salih, M., Austin, C., Warty, R. R., Tiktin, C., Rolnik, D. L., Momeni, M., Rezatofighi, H., Reddy, S., Smith, V., Vollenhoven, B., & Horta, F. (2023). Embryo selection through artificial intelligence versus embryologists: A systematic review. Human Reproduction Open, 2023(3), hoad031. https://doi.org/10.1093/hropen/hoad031