Protocol No: ECCT/25/06/03 Date of Protocol: 14-05-2025

Study Title:

Physiological and Biomarker Monitoring: Enhancing Vital Signs and Biomarkers Algorithm of Binah.AI in the Kenyan Population

Study Objectives:

Collection of subjects recordings using the Binah.ai Recorder, and vital sign data and blood samples using reference devices and lab tests to enhance the Binah.ai algorithm performance on the population of Kenya.

 

 

Laymans Summary:

This study wants to make sure the Binah.ai health application works well for people in Kenya. The application checks your vital signs like heart rate, oxygen levels, and blood pressure just by using a smartphone. To do this, researchers will record volunteers using the Binah.ai Recorder and also check their vital signs and blood using standard medical equipment and lab tests.

In total, 20,000 people from around the world will take part, including 1,000 people in Kenya at Zuri Health Medical Center. The information collected will help train the application to better understand health signs in different people. After this training, researchers will test the updated application to see if it gives more accurate results.

The goal is to make sure the application works well and gives reliable health readings for people in Kenya.

 

Abstract of Study:

This study aims to advance the accuracy and reliability of the Binah.ai algorithm in the Kenyan population. Subjects will be recorded using the Binah.ai Recorder and their vital signs (pulse rate, oxygen saturation and blood pressure) and blood samples will be collected using reference devices and lab tests. A total of 20,000 participants will be recruited globally (Italy, India, Israel, Japan, USA), including 1,000 participants that will be enrolled in Zuri Health Medical Center, Kenya. The process includes training the algorithm with the newly collected data. Once the training is completed, new data collected will be used to validate the performance of the improved algorithm. We expect that this data collection study will improve the overall algorithm performance among the Kenyan population.