FOOT PERIPHERAL NEUROPATHY

Smart, portable, non-invasive early detection of Foot Peripheral Neuropathy for safer diabetic foot care.

Comment

INSPIRATION

Diabetes-related foot complications are one of the most overlooked yet dangerous health issues, especially in communities where access to frequent clinical screening is limited. Foot Peripheral Neuropathy often begins silently, with symptoms such as numbness, reduced sensation, tingling, or unnoticed pressure points, which can later progress into ulcers, infections, and even amputations if left undetected.

We were inspired by the need to create a portable, affordable, and non-invasive solution that can help in the early screening of neuropathy-related foot risks before severe complications arise. Our goal was to bridge the gap between hospital-based diagnosis and day-to-day patient monitoring by designing a system that can support timely intervention, preventive care, and improved quality of life.

WHAT IT DOES

Our project is a smart diagnostic and monitoring system designed to assist in the early detection of Foot Peripheral Neuropathy and diabetic foot-related complications.

The system works by combining foot image analysis with patient-related clinical parameters such as:

  • Glucose level
  • Blood pressure
  • Skin thickness
  • Insulin level
  • BMI
  • Diabetes pedigree function
  • Age
  • Pressure-related foot observations (if available)

Using this information, the system can:

  • Analyze foot condition and identify visible abnormal patterns
  • Predict the possible risk of diabetic neuropathy-related complications
  • Support early identification of ulcer-prone or high-risk foot conditions
  • Provide visual outputs such as attention maps or heat-based interpretations for better understanding
  • Generate a simple diagnostic-style interpretation to assist users or clinicians

Ultimately, the project aims to serve as an assistive screening tool for preventive diabetic foot care.

HOW WE BUILT IT

We designed the project as a combination of medical screening logic, image processing, and machine learning-based prediction.

  1. Data Collection We worked with:
  • Foot ulcer / foot condition images
  • Clinical diabetic health parameters
  • Additional patient-specific indicators relevant to neuropathy risk
  1. Image-Based Analysis We used foot images as the visual input to identify:
  • Ulcer-related regions
  • Skin abnormalities
  • High-risk visible foot conditions

We explored methods such as:

  • Image preprocessing
  • Feature extraction
  • Thermal / MRI-style visualization concepts
  • Region-based analysis and explainable visualization methods like attention maps
  1. Tabular Health Prediction We integrated patient health parameters into a machine learning pipeline to predict neuropathy/diabetic complication risk.

This included:

  • Data preprocessing
  • Feature normalization
  • Classification-based prediction
  • Risk interpretation

  1. Integrated Decision Support We combined both visual analysis and clinical parameter analysis to create a more meaningful and realistic healthcare-oriented output rather than relying on a single input source.

  2. Prototype Vision The project was designed with future integration in mind for:

  • Cloud monitoring platforms
  • Remote health dashboards
  • Doctor-facing interpretation systems
  • Smart wearable / portable diagnostic ecosystems

CHALLENGES WE RAN INTO

One of the biggest challenges was that Foot Peripheral Neuropathy is not always directly visible in its early stages, which means that designing a meaningful detection system required us to think beyond just image classification.

Some key challenges included:

  • Finding a way to connect visible foot patterns with clinical diabetic risk indicators
  • Handling the limitation that some symptoms of neuropathy are sensory and internal, not purely visual
  • Designing a system that remains non-invasive while still being medically useful
  • Structuring a model that can work with both images and numerical health data
  • Ensuring the project remains practical, affordable, and scalable for real-world use

These challenges pushed us to think more carefully about how AI can be used responsibly in healthcare support systems.

ACCOMPLISHMENTS THAT WE'RE PROUD OF

We are proud that we were able to conceptualize and build a project that addresses a real and high-impact healthcare problem with a preventive and patient-centered approach.

Some of our key accomplishments include:

  • Developing a project focused on early diabetic foot risk detection
  • Combining image-based screening with clinical parameter analysis
  • Creating a non-invasive and portable healthcare support concept
  • Designing the system with future cloud and wearable integration potential
  • Building a solution that has value not only as a technical project but also as a socially meaningful healthcare innovation

Most importantly, we are proud that this project has the potential to contribute toward reducing severe diabetic foot complications through early awareness and intervention.

WHAT WE LEARNED

This project taught us that healthcare innovation is not just about building technology — it is about solving a problem in a way that is practical, understandable, affordable, and meaningful for real people.

Through this project, we learned:

  • How to work with medical-image-oriented problem statements
  • The importance of combining multimodal data instead of depending on a single source
  • How AI and ML can support preventive healthcare
  • The importance of explainability and interpretability in health-related systems
  • How to think beyond a prototype and design for real-world usability and future deployment

We also learned that some of the most impactful innovations are those that solve problems before they become emergencies.

WHAT'S NEXT FOR FOOT PERIPHERAL NEUROPATHY

This project has strong potential for expansion into a complete smart diabetic foot care ecosystem.

Our future plans include:

  • Improving model accuracy with larger and more diverse datasets
  • Integrating real-time sensor-based pressure analysis
  • Adding thermal imaging support for better abnormality detection
  • Connecting the system to cloud platforms for remote monitoring
  • Creating a mobile application for easier patient access and doctor review
  • Building a portable hardware prototype for on-field and home-based screening
  • Enhancing the system to support continuous diabetic foot health tracking

In the future, we envision this project evolving into a smart preventive healthcare device that can help patients, caregivers, and clinicians detect risk earlier and act faster.

Built With

Share this project:

Updates