# Anukriti AI

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Abhimanyu R B](https://cerebralvalley.ai/u/abhimanyurb)
- **GitHub:** https://github.com/Abm32/pathya
- **Demo video:** https://youtu.be/21679gHAEqc
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/270

Pathya (പഥ്യം) turns the pharmacogenomic lab reports India already produces into verified, de-identified research data. It is a research tool, not medical advice: it makes no dose, drug or test recommendation and has no patient-facing output.

The problem we know firsthand. Labs across India run these tests every day. The results leave as PDFs and WhatsApp photos in a dozen layouts, reach one hospital, and stop. India's DPYD frequency evidence rests on about two studies and 3,600 people, and the variants that matter here are not Europe's: the common Indian decreased-function DPYD variant is HapB3, not the *2A that European-derived panels are built around, and c.85T>C is carried by roughly a quarter of Indians.

How it works. Muse Spark 1.3 reads a report - PDF, phone photo, or results read aloud through Muse Voice Transcribe in mixed Malayalam and English - under a strict JSON schema, quoting every gene call from the page rather than paraphrasing it. PharmCAT 3.4, CPIC's reference engine, standardises the allele names. Deterministic rules then record which India-relevant variants the panel never tested, strip every identifier before storage, and a curator verifies each call against its own quote. Records pool into carrier frequencies with Wilson 95% intervals, where a panel that never tested a variant is left out of that variant's denominator instead of being counted as a non-carrier - the page shows both conventions, so the choice is visible rather than buried.

The same report can be read again by open-weights Muse Spark Open, so a biobank can run the whole pipeline inside its own network. That is the only version of this that could ever touch a real patient's report.

Validated on real data, not our own fixtures. 208 CDC GeT-RM reference DNAs, cross-checked against their 1000 Genomes 30x GRCh38 sequence: 622 of 646 gene calls agree with clinical-laboratory consensus (96.3%), exact on every CYP2C19, CYP2C9 and VKORC1 call, and every one of the 24 mismatches is traced and written up. The hosted and open-weights readers agree within one call across 580 comparisons. The benchmark also produced the project's own argument unprompted: six disagreements were decreased-function SLCO1B1 alleles the consensus panel never looked for, all six in African-ancestry samples - a panel designed elsewhere calling a carrier a non-carrier.

Live, open to everyone, no sign-in: https://pathya.anukritiai.com

## More from Meta Global AI Developer Hackathon

- [GOKU](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/267)
- [CodeSphere_PH](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/268)
- [IndieBug](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/269)
- [MediLens](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/271)
- [Muse Life Graph](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/272)
- [InkLoop](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/273)

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