AI-powered diabetic retinopathy screening — from your phone to a clinical second opinion.

🌐 Website  •  ⚙️ API  •  📈 Status

▶️ Watch the demo video

Fundusnap is a comprehensive medical-imaging solution that helps healthcare workers and patients detect and analyze diabetic retinopathy (DR) from fundus (retinal) images. A user captures a photo of the back of the eye with the mobile app, and Fundusnap returns an AI classification of disease severity, highlights the specific retinal lesions it found, and lets the user ask follow-up questions to an AI medical assistant that explains the result in plain language.

It is delivered as an end-to-end product spanning a mobile app, a backend API, a marketing/management website, and an offline AI model for low-connectivity environments.

Diabetic retinopathy is one of the leading causes of preventable blindness worldwide, and it disproportionately affects regions with limited access to specialist eye care.

• Too few specialists. Screening for DR traditionally requires an ophthalmologist to manually examine retinal images — a scarce and unevenly distributed resource, especially in rural and developing areas.
• Late detection. Early-stage DR is often asymptomatic. By the time patients notice vision problems, the disease may already be advanced and harder to treat.
• High screening cost & low throughput. Manual grading is slow and expensive, making large-scale population screening impractical.
• Results are hard to understand. Even when a patient receives a screening result, the clinical terminology is rarely accessible to non-experts, leading to poor follow-up.

Fundusnap brings specialist-grade screening to a smartphone and makes the result understandable to everyone:

1. Capture — The Flutter mobile app guides users to take a high-quality fundus image (with photo and video capture support).
2. Classify — The image is sent to the API, which runs it through Microsoft Azure Custom Vision to classify the severity of diabetic retinopathy.
3. Detect — A custom object-detection AI locates and bounds individual retinal artifacts/lesions (e.g. microaneurysms), so the result is explainable rather than a black box.
4. Explain — An AI medical chat assistant (Microsoft's Phi-4 model via OpenRouter) interprets the findings in simple, informative language and encourages appropriate follow-up with a healthcare professional — without making a clinical diagnosis.
5. Stay available offline — A separate, offline-capable image-classification model acts as a fallback for poor connectivity or primary-API outages, so screening keeps working where it's needed most.

All medical data is handled with security and compliance in mind (JWT-based auth, encrypted transmission, and secure image storage).

Fundusnap was built for and submitted to three national programs in Indonesia, achieving recognition in each:

Organized by Microsoft, the Biji-biji Initiative, and Telkom University, held at Tel-U Hub.

ElevAIte Indonesia is an AI-skilling initiative by Microsoft and the Biji-biji Initiative that aims to equip 1 million Indonesian talents with relevant AI skills for the era of digital transformation — free of charge and with no selection barrier. The program partners with government, industry, educational institutions, and communities to connect talent with new opportunities created by AI, such as improved productivity, creativity, and responsible innovation. It runs as a journey — from mastering AI fundamentals on Microsoft Learn and earning the Microsoft AI-900 certification, to a Hackathon where participants apply their AI skills to solve real-world problems, followed by an incubation phase. Fundusnap was developed and submitted during this hackathon stage and placed 3rd overall.

Organized by the Indonesia World Students Association (Perhimpunan Pelajar Indonesia Dunia / PPI Dunia).

Digination Competition 2025, themed "AI for All: Bridging Innovation and People," is a hackathon open to active undergraduate Indonesian students from universities around the world. Teams of three members from one university submit a paper and video to advance through the stages, competing across three impact tracks — Health, Education, and Social Business — for prizes of IDR 10,000,000 per track. Fundusnap competed in the health track and reached the Top 6 Finalists.

Organized by Yayasan BUMN, Microsoft, and the Biji-biji Initiative.

Pikiran Terbaik Negeri is a grant-competition created by Yayasan BUMN in partnership with impact-investment organizations, media partners, and the ANGIN Foundation. The program's mission is to identify, nurture, and develop social entrepreneurs (menemukan, membina, dan mengembangkan wirausaha sosial) who create meaningful impact for Indonesian communities and environmental sustainability. Beyond grants, participants receive bootcamp training to strengthen their entrepreneurial skills and networking opportunities with financiers in the impact sector. Fundusnap was selected into the Top 30.

🥉 3rd Winner Awarding The Fundusnap team receiving the Juara 3 award at the elevAIte Microsoft × Biji-biji Hackathon 2025, held at Tel-U Hub.	🏢 Fundusnap Booth The team demonstrating Fundusnap to visitors and judges at the exhibition booth, held during the competition day at Telkom University.

The entire system is designed with security and compliance in mind:

• Secure authentication using JWT (short-lived access tokens + refresh tokens)
• Encrypted data transmission
• Secure storage of medical images
• Privacy-conscious handling of medical data
• Regular security updates and patches

Each component has its own repository with detailed setup instructions. Please refer to the individual README files in each repository for specific setup and installation steps.

This project is licensed under the MIT License.

Fundusnap Developers dev@fundusnap.faizath.com