FootPrint Dx

Inspiration

Observing how diabetic foot disease (DFD) leads to devastating complications inspired our team to create a solution rooted in simplicity — one that could overcome the real-world challenges faced by patients in rural India and other underserved communities globally. We saw firsthand how early detection and continuous monitoring of diabetic foot ulcers (DFUs) can be life-saving — yet remains costly, time-consuming, and inaccessible for millions.

With no affordable or easy-to-use solution currently available, we asked ourselves a simple question: What if paper could function as a diagnostic surface? Could we create a tool that retained the clinical integrity of advanced medical devices, but was designed for the realities of low-resource environments?

Inspired by everyday test strips like pH paper and leveraging the ubiquity of smartphones, we set out to transform traditional diagnostics. Footprint Dx was born from this vision — a paper-based, AI-powered diabetic foot ulcer screening platform designed to empower both patients and physicians. Our approach pairs technological innovation with a user-friendly, low-cost solution — ensuring that early detection and preventative care are no longer a luxury, but a right accessible to all.

What it does

FootprintDX enables early detection and risk assessment of diabetic foot ulcers using an innovative yet simple process centered around paper-based chemical testing. Patients step onto a set of specialized diagnostic papers, each embedded with a different chemical reagent — carefully selected to react to specific biomarkers critical in identifying DFU risk.

These biomarkers include protein leakage from skin breakdown, pH imbalances that signal infection-prone environments, localized temperature variation indicating inflammation, and pressure distribution patterns that map high-risk zones on the foot.

The patient then captures an image of the developed paper imprint through the FootprintDX mobile app. Our AI-driven backend processes the image, corrects for lighting inconsistencies, segments the footprint, and quantifies the color gradients produced by the chemical reactions. The result is a patient-friendly report that communicates the risk level and recommended next steps. For physicians, a connected web portal allows for remote monitoring of patients over time — enabling preventative care and reducing the risk of serious complications.

How we built it

Building FootprintDX required an interdisciplinary approach, combining materials science, computer vision, and user-centered design. We started by selecting affordable, scalable paper materials and chemical dyes with proven colorimetric properties capable of detecting biomarkers relevant to diabetic foot complications. Iterative testing was conducted to ensure color changes were clinically meaningful, sensitive, and reliable across diverse environmental conditions.

On the technical side, we developed a Python-based computer vision pipeline using OpenCV. This allowed us to normalize for different lighting conditions, segment the footprint using the carbon paper imprint, and analyze localized color changes with precision. Gradient analysis models were created to quantify the severity of chemical reactions, transforming color intensity into actionable numerical values that inform risk stratification.

We built the mobile app to interface seamlessly with this backend, allowing users to capture images and receive results with minimal effort. The app supports multiple languages to accommodate diverse patient populations and includes a clean, medical-themed UI. Additionally, we developed a cloud-based physician dashboard to facilitate remote monitoring, report generation, and longitudinal patient tracking — completing the ecosystem for scalable diabetic care.

Challenges we ran into

Designing Footprint Dx came with a unique set of challenges that required both creative problem-solving and technical refinement. One of our most significant obstacles was ensuring accurate color detection and analysis across different smartphone cameras and lighting environments — a critical step for ensuring consistency and reliability in results. We addressed this by implementing a robust color normalization algorithm and testing the system across simulated real-world lighting conditions.

Another challenge involved maintaining reproducibility in the testing procedure. In order to track disease progression effectively, we needed patients to step on the paper in a standardized manner every time they used the test. To achieve this, we incorporated carbon paper underneath the diagnostic papers, capturing a physical footprint that guides image alignment and analysis across repeated tests.

Material stability was also a key concern, as the chemical papers needed to maintain their reactivity without degradation over time — especially when deployed in environments with fluctuating temperature and humidity. Finally, balancing advanced AI-driven analytics with an intuitive, patient-friendly user experience required deliberate design decisions to ensure accessibility for individuals with varying levels of health literacy.

Accomplishments that we're proud of

We are incredibly proud of building a functional prototype that proves the viability of low-cost, paper-based diagnostics powered by AI. Footprint Dx represents a novel integration of materials science and computer vision in a healthcare solution designed from the ground up for scalability and accessibility.

We successfully created a working image analysis pipeline capable of processing foot imprints from chemical reaction-based papers — a process that retains the diagnostic integrity of more expensive clinical systems at a fraction of the cost. The development of a multilingual, intuitive mobile app ensures that our solution is truly designed for patient empowerment, while the physician dashboard enables long-term remote monitoring and preventative care.

Perhaps most importantly, we are proud of how Footprint Dx demonstrates that innovation in healthcare does not always need to be high-tech in appearance — but must be high-impact in function.

What we learned

This project taught us the immense potential of frugal, user-centered innovation in healthcare. We learned that meaningful solutions arise not from adding complexity, but from simplifying the right components without sacrificing diagnostic quality. Working across disciplines — from chemistry and materials engineering to machine learning and UI/UX design — challenged us to think holistically about the product we were building.

We also learned the critical importance of designing for real-world constraints. Creating technology that functions in resource-limited settings — without laboratory infrastructure or specialized expertise — forces a level of creativity that ultimately leads to more adaptable and impactful solutions.

What's next for FootPrint Dx

Moving forward, our goal is to transition Footprint Dx from prototype to real-world deployment. Our next steps include conducting clinical validation studies in partnership with diabetic care clinics and public health organizations to evaluate accuracy, usability, and patient outcomes.

We aim to expand our machine learning models using larger, more diverse datasets to improve diagnostic accuracy across different patient populations and skin types. We will continue refining the chemical composition of the diagnostic papers for maximum stability and shelf-life, while targeting mass production at a cost of less than 30 rupees per unit.

In parallel, we plan to explore the expansion of Footprint Dx into additional diagnostic use-cases — such as infection monitoring, wound healing assessment, and hydration analysis — all built on the same foundation of affordable, paper-based testing paired with AI-driven analysis. Ultimately, we envision Footprint Dx as a scalable healthcare platform that bridges technology and accessibility — transforming preventative care for diabetic patients worldwide.