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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