Cleveland Clinic Innovations
Software that Assists Doctors in the Diagnosis of Autism in Children
Client
Cleveland Clinic Innovations works to elevate technologies in four main areas: Medical Device, Life Science, Delivery Solutions, and Health Information Technology. Their innovation managers and product development specialists have the experience to bring highly particular skills and knowledge to the table. When innovations are deployed through CCI's process, it ensures the most promising inventions are positioned to succeed in the ever-changing healthcare marketplace.
Summary
We partnered with Cleveland Clinic Innovations to build software that would support the clinical trial of a system designed by one of the leading doctors in autism research. We created a client application to perform eye tracking and a server side computational engine to perform the analysis of that "gaze data", computing scores which would help predict whether or not the participant is on the ASD spectrum.
Scope
Server Side Development
Software Using Complex Algorithms
Advanced Coding
User-testing
Native Windows Application Development
Understanding the Problem
The system was created to help support the diagnosis of autism spectrum disorder (ASD) in children. This is possible through the collection and analysis of eye tracking data, or gaze data, while participants are presented visual stumli.
Key Solutions
Our solution consisted of three main components, all of which were custom built software. Component #1 was a native Windows application that would present visual stimuli to participants and collect eye tracking data from the session. This data was then transmitted to a server-side computational engine (Component #2) which implemented complex scoring algorithms defined by the doctor in order to compute assessment scores. Finally, an administrative web interface (Component #3) was developed to provide access to the assessment scores and a significant amount of diagnostic data necessary for the clinical trial.
Throughout the development of the systems and their continued refinement through the clinical trial, our team's involvement grew well beyond just software implementation. We also collaborated with the doctor on the refinement of his statistical algorithms used in the analysis of the gaze data. Specifically, we worked with him to develop algorithms that would reduce "noise" and improve the quality of raw gaze data from the collection device.
Final Results & Takeaways
In the end, we became partners in both the development and the science behind this revolutionary system. As their ongoing implementation partner, we are excited to begin working on the next phase of the project's life cycle as it transitions from its clinical trial phase and becomes ready for commercialization by evolving into a clinical decision support (CDS) system.
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