Mayo Clinic is a renowned healthcare organization at the forefront of innovation. This case study explores our team's success in the science-research Kaggle project, which was focused on assisting physicians in stroke care. The concept was to develop a Computer Vision-based solution to etiology classification, in order to quickly identify stroke origin – all organized as a research competition by the Mayo Clinic.
The project aimed to effectively differentiate between major acute ischemic stroke etiology subtypes - cardiac and large artery atherosclerosis - which is crucial in determining appropriate treatment plans. Mayo Clinic sought to develop a reliable tool that could assist physicians in accurately classifying the origins of blood clots to optimize stroke care.
Leveraging our expertise in AI, Machine Learning, and Computer Vision, we developed a deep learning-based model that could classify the etiology of ischemic strokes from histopathological images.
Punktum's collaboration with Mayo Clinic led to the development of a cutting-edge deep-learning model that accurately classifies the origins of blood clots in ischemic stroke. In the Kaggle competition, the solution achieved an impressive 46th place among 896 teams, earning a silver medal. The developed solution is a great example of how deep tech can pave the way for more targeted and effective treatments, revolutionizing stroke care and improving patient outcomes.