# Sarah Traore

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Sarah T](https://cerebralvalley.ai/u/CybaroseDev)
- **GitHub:** https://github.com/Cybarose/myomatlas
- **Demo video:** https://www.youtube.com/watch?v=LOoYQ1xTpTA
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/145

Myomatlas is an open, image-based decision-support tool for abnormal uterine bleeding, one of the most common and most under-addressed problems in women's health. Heavy menstrual bleeding affects up to one in three women and is a leading cause of anemia, yet when fibroids are the cause, their exact location, which determines the right treatment and whether the uterus can be preserved, is often reported inconsistently and rarely explained to the patient. I built an end-to-end pipeline on the open UMD MRI dataset. A U-Net I trained segments the uterine wall, cavity and each fibroid from a pelvic MRI and measures each lesion's size and its position relative to the cavity and serosa. A Claude agent then reasons strictly from an encoded FIGO and PALM-COEIN guideline to classify each fibroid, show its justification, flag when malignancy exclusion is warranted, and derive management options, including uterus- and fertility-preserving ones. The result is presented as an interactive 3D view, a structured clinical report, and a plain-language patient explanation, with a PDF export. The key finding is that the same scan should not always lead to the same recommendation. When I add patient context, a 34-year-old who wants children versus a 58-year-old with postmenopausal bleeding, the agent's malignancy assessment and its ordering of treatment options change accordingly, from uterus-preserving management to flagging for malignancy exclusion. This matters because it makes the location-to-treatment-to-fertility logic explicit and understandable, for both clinicians and patients, in an area of medicine where women are too often unheard.

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