Real-Time AI-Based

Trauma Analysis

Our technology combines the best of several disciplines. In its core are state-of-the-art deep learning methods with architecture specifically designed to translate vehicle sensor data into medical insights.

This unique design is made possible with expertise in the fields of medicine, signal processing, biomechanics, and crash mechanics. These cutting-edge algorithms are deployed on our cloud architecture and delivered in real time using a range of client-side solutions.


A car accident
is comprised of three
different collisions

The car’s
collision with
the external
The body’s
collision with
the car
The internal
organs’ collision
within the body

The third collision is the one responsible for the different actual injuries passengers suffer. Understanding its details is crucial to understanding the passengers’ situation. Today, the existing technology supports and analyzes only the first accident, describing the forces that were applied on the vehicle alone.

Measuring the physical forces acting on passengers during a car crash, leveraging existing vehicle sensors

Analyzing a crash test result, the measurements show a big difference between the forces that were applied on the vehicle and the passenger – both in size, shape and time. This gap is inherent, leaving only poor correlation between the vehicle damage and the passengers’ injuries.

As each accident has 22 million parameters, this problem remained unsolved using mechanical tools and linear regressions that tried to explain the correlation.

MDGo has developed a set of ML models that are able to understand the connection and create an accurate prediction of the forces applied on the different organs. By utilizing existing sensors, MDGo’s technology generates a leapfrog in the trauma field using already existing sensors and infrastructure.