Scoliosis screening with AI for health visitors
Our company in short
Vitarex Studio Ltd is an independent software development company with a primary focus on creating software for Hungarian healthcare providers. Our mission is to provide high quality, modern technology to our customers and users. We have almost 70% market share with our software targeting health visitors, which is a special healthcare nursing segment in Hungary covering the whole country.
Our business problem and machine learning
Our company cooperates closely with the country’s health visitors, helping with the medical screening of school-aged children. The assessment of the posture of the children is carried out during such screenings. Introducing an ML-based software tool would help identify problematic cases and evaluate postures in general.
Our aim is to develop an application that can help with the assessment of postures by automating the process with ML models. For a well-functioning application, we must provide robust and reliable ML models. In the scope of the IML4E project this effort can be helped by developing an MLOps infrastructure with which models can be easily trained, evaluated and deployed, as well as by finding an efficient way to handle the different data.
Our research and solutions
In the scope of the IML4E project we have researched tools and methods that can help with keypoint detection model debugging and data management.
We have built a model training pipeline and developed a custom keypoint detection evaluation process to easily create and compare different ML models. In cooperation with Spicetech, virtual validation of the keypoint model was executed with VALICY, and this way the limits of the model had been determined.
A model deployment process was also created to easily deploy and update ML models in production. We have developed a pose assessment software that uses a keypoint detection model for posture analysis and carried out a field test with it. With the help of a built-in feature the performance of the ML model can be continuously monitored, and additional training data can be collected.
Future direction
We have gained valuable experience designing and developing ML based applications using MLOps practices. This experience will provide great benefits in other ML projects, some of which are already underway.
