Case Study: a leading predictive biotechnology research company achieves 98% accurate DILI prediction with Cogito Tech

leading predictive biotechnology research company boosts DILI model precision by 48% with Cogito Tech

A leading predictive biotechnology research company faced a challenge in developing its AI model for drug toxicity. Its efforts to detect Drug-Induced Liver Injury (DILI) were hindered by inconsistent annotations of clinical imaging data, which introduced ambiguity and limited the model's accuracy and interpretability. The company engaged Cogito Tech to resolve these annotation inconsistencies.

Cogito Tech implemented an iterative annotation workflow that emphasized area of interest segmentation and deep phenotype qualification. This solution provided the high-quality, expert-labeled data needed to retrain the AI model. The result was a 48% increase in precision and a final model accuracy of up to 98%, achieved at a fraction of the cost of traditional assays. Cogito Tech's work enabled the client to build a highly accurate and interpretable DILI risk model.