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MedIntel Nexus: Unified Medical Image Research Database

A free, collaborative hub where clinicians and AI researchers analyze, annotate and accelerate medical discovery using open medical imaging data. Vision: trustworthy, accessible imaging infrastructure that amplifies clinical insight and safe AI innovation.

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Challenge — Fragmented imaging slows progress

Medical image data is scattered across hospitals, registries and personal drives. Inconsistent formats, missing metadata and limited access hinder comparative diagnosis, reproducibility and robust AI training.

  • Inconsistent labels and formats (DICOM variants, JPEG, PNG)
  • Access barriers for cross‑institutional research
  • Limited tools for collaborative annotation and validation

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Solution — A centralised, open platform

Secure image upload & viewer

Support for X‑ray, CT, MRI and ultrasound with integrated DICOM tooling and fast web viewers.

AI-assisted tagging & similarity search

Automated disease labels, similarity retrieval and precomputed embeddings to speed case comparison.

Collaborative annotation & discussion

Threaded discussions, shared annotation layers and version history for multispecialty review.

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Core features — Practical, compliant, extensible

  • Smart categorization: body part, modality, disease taxonomy
  • DICOM import/export, automatic preprocessing pipelines
  • Auto anonymization and audit logs for privacy compliance
  • Forum and model leaderboard to encourage reproducible research

Designed for clinicians and researchers: clear metadata, provenance and easy dataset export.

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Tech stack (free tier) — pragmatic choices for reproducibility

1

Frontend

Streamlit + React components — rapid prototyping with interactive viewers.

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Backend

Django API with SQLite for metadata; modular for upgrade to Postgres.

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AI Tools

TensorFlow Lite for model sharing, OpenCV & scikit-learn for preprocessing.

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Storage & Hosting

Google Drive API, Streamlit Cloud, GitHub for code — maximising free quotas.

Open‑source-first architecture that scales from single researcher to institutional deployment.

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Collaboration & research workflow

01

Annotate

Clinicians mark regions, add structured labels and clinical notes directly on images.

02

Share

Controlled dataset sharing with provenance, consent metadata and anonymisation stamps.

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Train & Validate

Researchers download harmonised datasets or use hosted notebooks to train models reproducibly.

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Curate Knowledge Base

Community‑driven case libraries and validated model entries feed a living diagnostic resource.

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Impact — Clinical and global research benefits

30%

Faster diagnosis

Estimated reduction in time to consensus with shared cases and similarity search.

50%

Open research growth

Increased access to curated datasets for model validation in low‑resource settings.

Bridges clinical expertise and AI innovation, reduces diagnostic errors and creates equitable research access — especially valuable for developing countries with limited imaging datasets.

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Wireframe flow — From landing page to research hub

Research Hub

Image Viewer

Dashboard

Landing

1) Landing: project overview + secure sign-in. �2) Dashboard: filters, studies, quick stats. �3) Viewer: annotate, compare, AI suggestions. �4) Forum: peer review and discussion. 5) Research Hub: notebooks, model leaderboard and dataset exports.