Development of a Cross-Platform Mobile Trichoscopy Application with Enhanced Hardware Calibration and Image Processing

A healthtech organization requires a mobile diagnostic application capable of capturing and analyzing scalp and hair images, utilizing proprietary computer vision algorithms. The challenge includes integrating with a specialized hardware case that enhances image quality, managing diverse device camera capabilities, ensuring reliable data transfer in offline scenarios, and incorporating real-time support for medical professionals. Existing hardware limitations and performance constraints necessitate sophisticated calibration, fast image processing, and seamless user experience for accurate diagnosis and monitoring of hair-related conditions.

A medium-sized innovative healthtech company specializing in diagnostic solutions and medical imaging, focusing on hair and scalp analysis.

• Create a cross-platform mobile application for iOS and Android that interfaces with proprietary computer vision algorithms for hair analysis.
• Enable physicians to capture high-quality scalp and hair images with precise camera control, including manual lens selection and calibration procedures.
• Implement efficient image processing pipelines capable of correcting distortions caused by specialized hardware attachments, ensuring rapid analysis (targeting under 10 seconds per image).
• Develop hardware calibration techniques using imaging targets like Aruco boards and OpenCV-based algorithms to ensure accurate image recognition.
• Design robust offline data management solutions allowing storage and synchronization of examination and patient data.
• Integrate support channels such as live chat for immediate user assistance via SDKs like Intercom.
• Optimize app architecture for fast, responsive performance suitable for real-time diagnostics in clinical settings.
• Ensure high compatibility across Android devices with varying camera capabilities by implementing device hardware recognition and adaptive functionalities.

• Camera interface with manual lens selection and zoom control, tailored for high-quality hair imaging.
• Device hardware capability detection to adapt app functionalities to different device configurations.
• Calibration workflow utilizing Aruco markers and OpenCV-powered image analysis for precise camera calibration.
• Proprietary computer vision integration via API for automatic calculation of hair parameters such as hair count, density, length, and ratios.
• Background services for reliable data upload and synchronization when connectivity is available.
• Local database for caching examination records, patient profiles, and offline operations.
• Real-time support chat integration within the application for quick user assistance.
• Advanced image processing pipeline optimized with native C++ code through Dart FFI to minimize processing time.

• API connection to proprietary computer vision algorithms for hair analysis
• Hardware calibration procedures utilizing image recognition for hardware setup
• Real-time chat SDK for user support
• Cloud storage and synchronization services for data backup and transfer

• Fast image processing with a target time of less than 10 seconds per analyzed photo
• High app stability and reliability across diverse devices, with failure and error logging
• Secure handling of sensitive patient data and compliance with data protection standards
• Offline operation capability with seamless synchronization upon reconnection
• Scalable architecture supporting potential future feature expansion

The developed mobile application aims to significantly improve diagnostic accuracy and efficiency in hair and scalp analysis, reducing image processing time from potentially several minutes to under 10 seconds. It will enhance clinical workflows via reliable offline data management, device adaptability, and integrated support channels. The project is expected to enable healthcare providers to deliver rapid, objective assessments, improving patient outcomes and operational productivity in hair diagnostics.