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#melanomadetection
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Skinia: AI-Driven Melanoma Detection and Evolution Analysis App

Developed Skinia, an AI-powered app for detecting and analyzing the evolution of melanoma, during the StartUpShip program by Tec de Monterrey and Mondragon Universitate.

Oct 2023

Timeline

Creator

Role

Oct 2023

Timeline

Creator

Role

Summary

The StartUpShip program, organized by Tec de Monterrey and Mondragon Universitate, focused on fostering entrepreneurial skills to create startups with a triple impact: economic, social, and environmental. Skinia was conceived in this program as a solution to address the growing concern of skin cancer, specifically melanoma, by leveraging AI technology to provide early detection and continuous monitoring, thereby promoting health awareness and preventive care.

Goals

AI-Based Detection

Develop an app that uses AI to detect melanoma from skin images.

AI-Based Detection

Develop an app that uses AI to detect melanoma from skin images.

Evolution Analysis

Implement algorithms to analyze the progression of detected melanomas over time.

Evolution Analysis

Implement algorithms to analyze the progression of detected melanomas over time.

User-Friendly Interface

Design an intuitive interface for users to easily upload images and track changes.

User-Friendly Interface

Design an intuitive interface for users to easily upload images and track changes.

Health Awareness

Create educational resources to inform users about melanoma and skin health.

Health Awareness

Create educational resources to inform users about melanoma and skin health.

Awards

ThermoFlex Pitch Deck

Solutions

AI Algorithms

The core of Skinia involves sophisticated AI algorithms capable of detecting melanoma from skin images. Using machine learning models trained on a diverse dataset of skin lesion images, the app can accurately identify potential melanomas and analyze their progression over time. The AI processes the uploaded images, provides diagnostic results, and tracks changes, offering a reliable tool for early detection and continuous monitoring.

AI Algorithms

App Interface

Built using Swift and Xcode, the app interface is designed to be user-friendly and intuitive. Users can easily upload images of their skin lesions, which are then processed by the AI. The results are displayed clearly, indicating whether melanoma is detected and showing any changes over time. The interface also provides detailed information about the diagnosis, helping users understand the results and take necessary actions.

App Interface

Educational Content

Skinia includes a comprehensive learn section dedicated to educating users about melanoma and skin health. This section covers the basics of melanoma, its risk factors, prevention tips, and the importance of early detection. By offering educational resources, the app not only aids in diagnosis but also empowers users with knowledge about their health and the technologies used in modern medicine.

Educational Content

Tools

Xcode

Swift

Figma

Firebase

CoreML

TensorFlow

Responsabilities

Project Planning and Management

UI/UX Design

AI Model Development

App Development

Testing and Validation

Documentation

Insights by Juan Luis

Working on this project together with a multidisciplinary team was enriching, even more so when working with new tools and frameworks that introduced me to the world of machine learning for disease detection.

Juan Luis

Creator of Skinia

Conclusion

The development of Skinia during the StartUpShip program exemplifies the potential of technology-driven solutions to create a triple impact: economic, social, and environmental. By leveraging AI and machine learning, the app provides a quick and accurate method for detecting and monitoring melanoma, potentially aiding in early diagnosis and treatment. The inclusion of an educational component further enhances the app’s value, empowering users with knowledge about skin health. This project highlights the effective use of Swift, Xcode, and Apple frameworks to create a practical, user-friendly, and impactful tool in the field of dermatology and preventive healthcare.

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