Skinalyze
Skinalyze
Skinalyze is an app that uses machine learning to help users detect and understand their acne conditions. With a simple three-angle face scan, Skinalyze identifies acne types like pustules, papules, whiteheads, and blackheads, and provides a detailed severity analysis ranging from healthy to severe. The app offers personalized ingredient recommendations based on skin concerns detected during the scan. Additionally, users can log and compare their facial scans over time, using a slider tool to visually track progress and determine whether their skincare routine is improving their skin's condition or not. Skinalyze aims to make skincare more effective by offering tailored insights and guidance.
Research
Research
The development of Skinalyze began with an in-depth study of dermatological practices and acne classification systems to ensure the app's recommendations were both accurate and medically relevant. Input from dermatologists and skincare professionals shaped the criteria for analyzing acne severity and skin health. Surveys and user interviews further helped identify common skincare concerns and challenges, such as difficulty understanding ingredient labels or tracking skincare progress. This user-focused research informed the app's personalized recommendations and progress-tracking features.
Market research revealed a growing demand for accessible skincare tools that use advanced technology. Competitor analysis highlighted gaps in existing apps, such as a lack of real-time progress tracking and ingredient-specific guidance. This drove the decision to incorporate machine learning and intuitive design elements, setting Skinalyze apart in the crowded skincare tech landscape.
The development of Skinalyze began with an in-depth study of dermatological practices and acne classification systems to ensure the app's recommendations were both accurate and medically relevant. Input from dermatologists and skincare professionals shaped the criteria for analyzing acne severity and skin health. Surveys and user interviews further helped identify common skincare concerns and challenges, such as difficulty understanding ingredient labels or tracking skincare progress. This user-focused research informed the app's personalized recommendations and progress-tracking features.
Market research revealed a growing demand for accessible skincare tools that use advanced technology. Competitor analysis highlighted gaps in existing apps, such as a lack of real-time progress tracking and ingredient-specific guidance. This drove the decision to incorporate machine learning and intuitive design elements, setting Skinalyze apart in the crowded skincare tech landscape.
Research
The development of Skinalyze began with an in-depth study of dermatological practices and acne classification systems to ensure the app's recommendations were both accurate and medically relevant. Input from dermatologists and skincare professionals shaped the criteria for analyzing acne severity and skin health. Surveys and user interviews further helped identify common skincare concerns and challenges, such as difficulty understanding ingredient labels or tracking skincare progress. This user-focused research informed the app's personalized recommendations and progress-tracking features.
Market research revealed a growing demand for accessible skincare tools that use advanced technology. Competitor analysis highlighted gaps in existing apps, such as a lack of real-time progress tracking and ingredient-specific guidance. This drove the decision to incorporate machine learning and intuitive design elements, setting Skinalyze apart in the crowded skincare tech landscape.
Development
Development
Skinalyze was built around a core feature: using machine learning to analyze three-angle facial scans for acne detection. The development team trained custom models with CreateML, feeding them a dataset of annotated acne images to accurately identify conditions like pustules, papules, whiteheads, and blackheads. The Vision Framework was implemented to process and align facial scans, ensuring precision across all angles.
A key feature of Skinalyze is its severity analysis, which rates acne conditions from healthy to severe. This analysis powers the app's personalized skincare guidance, offering users ingredient recommendations tailored to their unique skin concerns. The app also includes a time-lapse tracking tool that lets users log facial scans and use a slider feature to visually compare progress. Extensive testing with focus groups ensured the app was user-friendly, with accurate results and a seamless interface.
Skinalyze was built around a core feature: using machine learning to analyze three-angle facial scans for acne detection. The development team trained custom models with CreateML, feeding them a dataset of annotated acne images to accurately identify conditions like pustules, papules, whiteheads, and blackheads. The Vision Framework was implemented to process and align facial scans, ensuring precision across all angles.
A key feature of Skinalyze is its severity analysis, which rates acne conditions from healthy to severe. This analysis powers the app's personalized skincare guidance, offering users ingredient recommendations tailored to their unique skin concerns. The app also includes a time-lapse tracking tool that lets users log facial scans and use a slider feature to visually compare progress. Extensive testing with focus groups ensured the app was user-friendly, with accurate results and a seamless interface.
Development
Skinalyze was built around a core feature: using machine learning to analyze three-angle facial scans for acne detection. The development team trained custom models with CreateML, feeding them a dataset of annotated acne images to accurately identify conditions like pustules, papules, whiteheads, and blackheads. The Vision Framework was implemented to process and align facial scans, ensuring precision across all angles.
A key feature of Skinalyze is its severity analysis, which rates acne conditions from healthy to severe. This analysis powers the app's personalized skincare guidance, offering users ingredient recommendations tailored to their unique skin concerns. The app also includes a time-lapse tracking tool that lets users log facial scans and use a slider feature to visually compare progress. Extensive testing with focus groups ensured the app was user-friendly, with accurate results and a seamless interface.
Tech Stack
Tech Stack
The app was developed using Swift and SwiftUI for a clean and responsive user interface, ensuring smooth interactions and intuitive navigation. The machine learning models were trained using CreateML, with the Vision Framework handling real-time image processing and facial alignment.
For data management, SwiftData and CloudKit were employed to securely store user logs, scan results, and progress data. AppStorage provided local caching for quick access to recent scans. The app was optimized for cross-device usability with iCloud sync, enabling users to track their skincare journey seamlessly across multiple devices. TestFlight facilitated beta testing, allowing real users to provide feedback that informed iterative improvements.
The app was developed using Swift and SwiftUI for a clean and responsive user interface, ensuring smooth interactions and intuitive navigation. The machine learning models were trained using CreateML, with the Vision Framework handling real-time image processing and facial alignment.
For data management, SwiftData and CloudKit were employed to securely store user logs, scan results, and progress data. AppStorage provided local caching for quick access to recent scans. The app was optimized for cross-device usability with iCloud sync, enabling users to track their skincare journey seamlessly across multiple devices. TestFlight facilitated beta testing, allowing real users to provide feedback that informed iterative improvements.
Tech Stack
The app was developed using Swift and SwiftUI for a clean and responsive user interface, ensuring smooth interactions and intuitive navigation. The machine learning models were trained using CreateML, with the Vision Framework handling real-time image processing and facial alignment.
For data management, SwiftData and CloudKit were employed to securely store user logs, scan results, and progress data. AppStorage provided local caching for quick access to recent scans. The app was optimized for cross-device usability with iCloud sync, enabling users to track their skincare journey seamlessly across multiple devices. TestFlight facilitated beta testing, allowing real users to provide feedback that informed iterative improvements.
musawahaidar123@gmail.com
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musawahaidar123@gmail.com
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