ammon_coder123
AI & Data Science ATCMboniLab AI
Problem Statement
Microscopic diagnosis of infectious diseases such as malaria, tuberculosis, and intestinal parasites relies heavily on manual examination of diagnostic slides. This process is time-consuming and prone to human error, particularly in rural and underserved health facilities where trained laboratory technicians are limited. High workloads and diagnostic delays may result in missed infections, false results, and delayed treatment, creating a significant challenge for timely and reliable disease diagnosis.
Proposed Solution
The proposed solution is an AI-powered mobile application that uses computer vision through both Edge AI and Cloud AI to analyze microscopic slide images and detect infectious pathogens such as malaria parasites, tuberculosis bacteria, and intestinal parasites. Edge AI will enable rapid offline analysis on the mobile device, while Cloud AI will provide advanced analysis when internet connectivity is available. The application will support laboratory technicians in making faster and more consistent diagnostic decisions, particularly in rural and resource-limited healthcare facilities.
Innovation
The innovation is an AI-powered mobile application that brings microscopic pathogens detection directly to the point of diagnosis using edge AI.The system processes microscopic images locally on the mobile device, enabling rapid and offline detection and classification of infectious pathogens without depending on the internet connectivity.Thus, it makes services more easier in rural areas where resource is limited.
Expected Impact
The system is expected to reduce the time and workload associated with manual microscopic examination, while supporting more consistent and timely pathogens detection. By operating on offline mode in mobile devices, it can support AI-assisted diagnostic to rural and resource limited facilities and improve access to healthcare services.