Image Classification of Stroke Blood Clot Origin using Deep Convolutional Neural Networks and Visual Transformers
This addresses the need for accurate stroke etiology classification to aid in treatment decisions, but it appears incremental as it applies existing methods to a specific medical domain.
The paper tackled the problem of classifying stroke blood clot origin into cardiac or large artery atherosclerosis subtypes using AI, achieving results with deep neural networks and visual transformers, but no concrete numbers are provided in the abstract.
Stroke is one of two main causes of death worldwide. Many individuals suffer from ischemic stroke every year. Only in US more over 700,000 individuals meet ischemic stroke due to blood clot blocking an artery to the brain every year. The paper describes particular approach how to apply Artificial Intelligence for purposes of separating two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis. Four deep neural network architectures and simple ensemble method are used in the approach.