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Case Study

Predictive modeling to identify digital biomarkers to accelerate oncology diagnosis for a medical device

Machine learning to maximize signal-to-noise to differentiate between normal and malignant tissue.


Our client has a nano mechanical sensor in clinical trials that can differentiate cell types in patient biopsies. This innovation promises to accelerate the diagnosis of cancer and offer personalized treatment recommendations. Our client needed data science driven analysis of the machine signal to identify mechanical biomarkers to differentiate normal and malignant tissue.


We engineered features to quantify machine signal and applied clustering, regression and machine learning analysis. We built a data engineering pipeline to handle large scale measurement data. We trained a predictive model to differentiate between normal and malignant cells, providing insight into the most important mechanical biomarkers. We are now partnering with our client to plan their MLOps infrastructure to deploy and manage predictive models in production, edge deployed within clinical environments.

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