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Mohammed Nasur: Building the Future of IVF – Synthetic Data for Sperm Assessment
Aug 17, 2026, 17:47

Mohammed Nasur: Building the Future of IVF – Synthetic Data for Sperm Assessment

Mohammed Nasur, Senior Ai/ML Engineering at Saal.ai, shared on LinkedIn:

“Building the Future of IVF: Synthetic Data for Sperm Assessment

As an AI engineer working at the intersection of computer vision and reproductive medicine, I’m excited to share the progress of IVF-AI, a synthetic data generation pipeline for sperm morphology and DNA fragmentation analysis.

With input from clinical embryologists and using WHO 6th Edition criteria, we’ve built a Blender-based system that generates:

  • 5 morphology classes (normal, head defect, neck/midpiece defect, tail defect, cytoplasmic droplet)
  • 4 DNA fragmentation grades (big/medium/small/no halo — SCD test simulation)
  • 500+ labeled samples with pixel-perfect segmentation masks and bounding boxes
  • Multi-task labels — each sample carries both morphology AND fragmentation ground truth

Why this matters:

Real IVF data is scarce, expensive, and privacy-constrained. Manual annotation is subjective and time-consuming. Synthetic data offers a scalable, privacy-compliant alternative — and when built with clinical input, it can be clinically relevant.

Our validation confirms:

  • Foreground pixels increase monotonically with halo grade (big → no halo)
  • Mask areas remain consistent the halo is visible in the image but excluded from segmentation labels
  • All 20 morphology × fragmentation pairs are represented

Next step: Motility

Now extending the pipeline to generate 10–20 second video clips of motile sperm with realistic flagellar beating patterns.

This will enable:

  • AI models for sperm motility assessment
  • Time-series analysis
  • Video-based WHO motility grading.”

Proceed to the video attached to the post.

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