Case Study: University of Helsinki improves Huntington’s disease image analysis with Aiforia

University of Helsinki analyzes mHtt with Aiforia using less than 15 images

The University of Helsinki, represented by doctoral student Polina Stepanova, faced a challenge in its Huntington's disease research. The existing methods for detecting mutant huntingtin (mHtt) aggregates in brain tissue were time-consuming, subjective, and produced unreliable data that was difficult to reproduce, hindering the development of new therapies. To address this, they implemented Aiforia's AI-powered software, Aiforia Create.

Using Aiforia's solution, the team trained an AI model with a relatively small set of annotated images to automate the detection and analysis of mHtt aggregates. This implementation by Aiforia provided unbiased, truthful analysis and highly accurate cell detection. The results were processed much faster than with traditional methods, saving researchers valuable time and delivering reproducible, statistically accurate data crucial for advancing their research.

University of Helsinki