Optimizing and Deploying a Contamination Detection SDK for Enhanced Surface Microbial Assessment

The client currently utilizes an outdated contamination detection algorithm integrated into a mobile application, which relies on hardware-dependent manual processes and has performance bottlenecks. The existing system exhibits slow analysis times (up to 2 minutes per assessment), crashes due to memory limitations, and limited customization options, hindering scalability and user reliability in critical infection control environments. In addition, manual control of lighting sequences increases human error risk, affecting surface assessment accuracy.

A mid-sized healthcare technology organization specializing in infection control and surface cleanliness assessment solutions for high-traffic environments, seeking to modernize their detection algorithms and expand product offerings.

• Modernize the existing contamination detection algorithm by migrating it to a contemporary, high-performance technology stack.
• Reduce surface assessment analysis time from approximately 2 minutes to under 3 seconds.
• Enhance system stability by enabling automatic image scaling to prevent crashes related to memory constraints.
• Automate control of lighting hardware (UV and LED bulbs) via seamless integration to ensure correct sequencing and reduce human error.
• Introduce customizable parameters (e.g., hue) to improve detection precision for surface contamination.
• Enable automatic camera parameter adjustment for improved image quality across different device models.
• Develop the solution as a standalone, whitelabelable SDK adaptable for various clients and integration scenarios.

• Migration of the proprietary contamination detection algorithm to a modern, high-performance stack utilizing Swift and GPU programming (e.g., Metal Shading Language).
• Parallel processing of multiple high-resolution images utilizing GPU and CPU to speed up analysis from minutes to seconds.
• Automatic image scaling to accommodate device memory limits, preventing application crashes.
• Integration with hardware control protocols (e.g., Bluetooth) to automate UV and LED lighting sequences for proper surface illumination.
• Configurable detection parameters, including hue and other relevant thresholds, to refine microbe detection accuracy.
• Automatic setup of camera parameters such as focus, ISO, white balance, and exposure using device capabilities (e.g., AVFoundation).
• Utilization of OpenCV or similar libraries for image processing and analysis.

• Bluetooth integration for remote control of UV and LED lighting hardware
• Camera APIs for automatic adjustment of imaging parameters
• Potential integration points for existing facility maintenance or reporting systems

• Analysis speed of under 3 seconds per surface assessment
• High system stability with minimal crashes under heavy usage
• Compatibility with various iPad models and screen resolutions
• Secure handling of hardware controls and data privacy standards
• Scalability to support deployment across multiple high-traffic facilities

By migrating and optimizing the contamination detection algorithm into a robust, high-speed SDK, the client can significantly improve assessment turnaround times from 2 minutes to under 3 seconds. This efficiency gain enables rapid decision-making in infection control, enhances accuracy and reliability through automated hardware control and parameter customization, and facilitates scalable deployment across multiple facilities. The solution supports client differentiation through whitelabeling capabilities, potentially increasing market reach and customer satisfaction.