🤝 Top Collaborators
🤓 Latest Submissions
Ovia: Ovarian Health Awareness Screening
Ovia is an AI-powered clinical decision support application built to close a critical gap in women's health: up to 70% of PCOS cases go undiagnosed, and ovarian cancer still has no recommended routine screening test for average-risk women. Unlike single-condition tools, Ovia screens for multiple ovarian conditions, PCOS, ovarian cysts, and ovarian tumors, by combining clinical history, menstrual and symptom data, and optional ultrasound image analysis through a multimodal AI pipeline. Each screening result is transparent by design: independent model outputs (BiomedCLIP for morphology, ConvNeXt for ultrasound appearance, XGBoost for clinical fusion scoring, and U-Net++ for lesion segmentation) are synthesized into a clear, explainable report, never a black-box score. Every result includes its reasoning, its limitations, and a recommended next step, reinforcing that Ovia supports early awareness and confirmatory follow-up, not diagnosis. Built with a privacy-first architecture, Ovia is designed to make earlier, more accessible ovarian health screening possible for women everywhere.
10 Aug 2026
Jaga — Cough-Based TB Triage
In 2024, around 2.4 million people who developed tuberculosis went uncounted—undiagnosed or unreported (WHO Global Tuberculosis Report 2025). Jaga, built by team Singkong, is an investigational phone-first triage prototype that helps close that gap. A community health worker records one guided cough and enters supported clinical inputs; Jaga returns a follow-up urgency estimate so programmes can prioritize who to send for confirmatory TB testing first. No X-ray machine, lab, or radiologist required. Two independent, never-fused AI pathways drive it. Gema handles cough plus clinical data: a Rust YAMNet gate confirms the audio is a cough, a local WavLM Large model embeds it entirely on-device, and XGBoost combines that with demographics. Prisma is a separate digital chest X-ray path (DenseNet121 + CLAHE) with its own estimate and a Grad-CAM heatmap. AMD is load-bearing: both models were trained on AMD Instinct MI300X via AMD Developer Cloud (ROCm PyTorch), and all model inference runs locally in Rust and Python. Fireworks serves only the Gemma guidance chat. Jaga does not diagnose or rule out TB. Every symptomatic person is directed to confirmatory testing—the estimate only prioritizes urgency.
13 Jul 2026
