Accessible, AI-Enabled TeleMedicine Solution for Multi-Organ Dysfunction Caused by SARS Infections

Overview

There are currently about 455 million SARS infections worldwide with 24% of these cases being severe and a 2% mortality rate due to late medical attention. SARS patients have common symptoms of Multi-Organ Dysfunction (MOD) which include: fatigue, difficulty breathing, and fever. Delay in the detection, diagnosis and treatment of MOD indicators can lead to the inability of managing severe symptoms, conditions worsening, and even deaths that can be prevented. The spirometer, oximeter, and thermal camera are the devices that measure these defined symptoms and were utilized in this solution to output correlative analysis. Code developed in C with the Arduino IDE is used to develop the correlation algorithm to output an “Overall Health” reading for the user to interpret. Integration of these vital elements led to a contactless telemedicine device that can display comprehensive data/results along with the use of Io Tand Machine Learning. Lung Capacity, Oxygen Saturation, and temperature reading had an inaccuracy of approximately 1-5%. The correlative analysis along with predictive analytics powered by AI/ML algorithms implemented, provides a precise overall health reading for users to apprehend. With the compact and affordable design of integrating the 3 biotechnologies onto a single board, we can leverage this to be accessible for low-income, senior citizens and underserved communities to ensure equal healthcare access opportunities to all people in our societies. In addition, a more sustainable flow of ICU admissions can be achieved because users will have real-time data on their state of being. This leads to a proper triage process when users will see a trend of their overall health decreasing and go to the hospital to seek medical attention.

Competition history

• JSHS 2022 Category not listed

Resources

• National JSHS abstract book ↗

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