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Patient Self Entry - where they can not only upload the images of their foot but also include their lifestyle factors
Inspiration
Mrs. Devi (name changed) , a 54-year-old grandmother from rural Tamil Nadu, spent her days caring for her 3 grandchildren while her daughters worked in town. One day, she noticed a foul odor from a sore on her foot—but brushed it aside. Weeks later, while toiling in the fields, she noticed blood oozing from the wound. Alarmed, her daughters rushed her to the local PHC. The diagnosis: Type 2 diabetes and a severe foot infection. She will never forget what the doctor said—amputation was the only option.
That moment didn’t just take part of her leg-it took her freedom,the joy of playing with her grandkids.
But what if Devi had a tool in her pocket—a simple app that could scan her foot and alert her when something was wrong? What if it could have saved her limb & her everyday joys?
That’s why this app matters. It’s not just about technology—it’s about giving millions like Devi a chance to catch the unseen, & take control of their health before it’s too late.
What it does
We have built an AI-powered mobile app that helps bridge the gap between patients and doctors in monitoring diabetic foot ulcers. Patients can easily upload images of their feet, which the app analyzes using advanced AI to detect and grade ulcers. This data is then sent to the treating physician as well.This allows the physician to keep track of the patients progress remotely.
How we built it
The application is a Flask-based web platform built with Python 3, featuring modular architecture using Blueprints for roles like patient and doctor. It uses Flask-SQLAlchemy with SQLite (development) and PostgreSQL (production) for data management, with migrations handled via Flask-migrate. The frontend leverages Jinja2 templating, Bootstrap 5, and Flask-WTF for secure, styled forms. Core features include role-based authentication, patient-doctor data entry, image-based AI observations using Google Gemini Vision API, and a real-time chat system using Flask-SocketIO. The project is version-controlled with GitHub and prepared for deployment on Render.com with secure environment configuration.
Challenges we ran into
API integration,Debugging the app,Ensuring credibility of the datasets
Accomplishments that we're proud of
We’re glad we were able to brainstorm & build an app in such a short span of time.
Collaborating with a technically skilled teammate gave us valuable insight into how powerful technology can be in developing innovative solutions to medical challenges, such as the detection of diabetic foot ulcers.”
What we learned
User friendly design and data security are as critical as clinical accuracy in medical AI apps.The most crucial aspect of any project in being successful is acceptance among the patients.
Through this project, we learned how impactful interdisciplinary collaboration can be—combining technical expertise with medical knowledge allowed us to explore how AI can play a crucial role in addressing real-world healthcare challenges like detecting diabetic foot ulcers
What's next for Diatech 10x Salvaging Limbs
Scaling to support multi-language access & integrating with hospital EHR systems.We also look forward to getting our idea implemented at the primary health care level.
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