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Reproductive healthLecture

Artificial intelligence, femtech and automation in IVF

29 minAudio: MultiSubtitles: EN · UA · RU
Player.aboutHeading

What was shown

A tour of where artificial intelligence already adds clinical value across the IVF cycle, with an explicit focus on the endometrium. The talk covers deep learning on imaging, femtech ecosystems for at-home hormonal screening, automation in routine embryology, microfluidics, and the emerging endometrium-on-chip model.

Key findings

Hertility, a UK accredited at-home femtech ecosystem, currently screens 18 reproductive conditions including 5 with direct endometrial relevance, combining patient questionnaire, home hormonal tests and AI-assisted teleconsultation. A convolutional model classifying endometrial receptivity from ultrasound reaches 95% accuracy on 402 images. AI-based CD138 counting for chronic endometritis matches expert pathologists in sensitivity, specificity and accuracy. AI-assisted hysteroscopy flags septum margins, ostia and high-perforation areas. Endometrial single-cell atlases (interactive, QR-linked) are now public. An endometrium-on-chip model with stroma, epithelium and vasculature responds to E2/progesterone dosing in a dose-dependent way.

What this means in practice

Treat AI as a gradual evolution layered on existing workflows, not a revolution. Use computer-vision tools in ultrasound and hysteroscopy where they are validated; consider AI-assisted CD138 reporting if histopathology volumes justify it. Position femtech ecosystems as a data-collection and patient-engagement layer that feeds into the clinic, not a replacement for clinical judgement. Build trust before scope: explainability and governance precede deployment.

Caveats

All endometrial-receptivity classifiers cited are still small-sample and need external validation. Data quality and standardisation across centres are the hard part; without them, AI inherits the noise. Ethical and data-protection frameworks for femtech-clinic data flows are still maturing.
Topics covered
  • Big data in IVF
  • Femtech and at-home hormonal testing
  • Endometrial ultrasound classification
  • CD138 detection in chronic endometritis
  • AI-assisted hysteroscopy
  • Endometrial single-cell atlases
  • Endometrium-on-chip models
  • IVF lab automation
  • AI limitations and data governance
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About the course

From at-home hormonal screening and ultrasound classifiers to CD138 detection in chronic endometritis and endometrium-on-chip — a tour of where artificial intelligence already adds value across the IVF cycle, and where its real limits sit today.

Mykola Gryshchenko

Mykola Gryshchenko

Professor, MD, PhD, and Head of Sona Academy at Sona Group; an expert in reproductive medicine focusing on medical education and scientific strategy. He initiates and leads the group's educational and scientific programmes, including the Winter School of Reproductology and satellite scientific events.

Author of the lecture "Artificial intelligence, femtech and automation in IVF"

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