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The proposed solution involved developing a deep learning-based model that accurately classified the etiology of ischemic strokes based on histopathological images.

Before the project, stroke etiology classification was primarily carried out by highly specialized doctors. Mechanical thrombectomy, a standard treatment for acute ischemic stroke, allowed for the analysis of retrieved clots. However, applying deep learning-based methods to predict stroke etiology and clot origin presented challenges due to the unique data formats and large image sizes.

The objective is to enable healthcare providers to better identify the origins of blood clots in deadly strokes, leading to improved post-stroke therapeutic management and reduced chances of recurrent strokes.

The team’s success was built on their expertise in different technical areas. First, they were skilled in image processing, which helped them handle and normalize histopathological images despite variations in lighting, quality and staining caused by different sources of data. This ensured consistency and accuracy during analysis.

Next, their knowledge of deep learning and neural networks was crucial in constructing and training highly accurate classification models. They used these models to predict the origins of blood clots in stroke cases based on the digital histopathological images.

To ensure the reliability of their models, the team also had strong skills in model training and testing. They used techniques like cross-validation to evaluate how well their models performed on new and unseen data.

Moreover, the team’s decision-making abilities in regard to deep learning models played a vital role in their success. They knew how to select the right hyperparameters, optimization algorithms, and loss functions, which significantly influenced the model’s performance and the overall results achieved.

The team divided themselves into subgroups based on their tasks and interests. One subgroup took charge of image preprocessing, ensuring the images were prepared and suitable for analysis. Another subgroup focused on building and training the deep learning models, trying out different configurations to improve accuracy. The third subgroup was responsible for submitting their predictions and ensuring that the model’s performance met the competition’s goals and that the model can handle unseen data.

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“It was our first encounter with such large images. Some of these images were massive, reaching up to 2 GB in size. It was quite a challenge even to open them, let alone process, analyze, and evaluate them to determine the type of stroke. Handling these images required innovative approaches and techniques to ensure accurate classification.” 


The way we approach and tackled the problem

In conclusion, our primary aim was to create a tool that would assist healthcare providers in their decision-making process. Our intention was not to replace the expertise of a doctor but rather to complement their skills and knowledge. The developed algorithm could indicate potential stroke etiologies with a high level of confidence, providing valuable insights to aid doctors in making faster and more informed decisions.


One of the top such solutions in the world

A very innovative approach to tackle the problem which resulted as one of the top solutions we took into account with further studies on the Etiology Classification aspects. The team was very proactive and communication was spot on.

***** *****

Project Science Lead

Anonymous

Learn how DAC.digital worked with Mayo Clinic and how they can support your HealthTech product and team.

DAC.digital Success Story in HealthTech for Mayo Clinic - A case study | 2024

The challenge was to accurately classify the origins of blood clots in ischemic stroke using histopathological images. The task involved differentiating between two major acute ischemic stroke etiology subtypes: cardiac and large artery atherosclerosis. 

The goal was to provide physicians with a reliable tool to assist in stroke etiology classification and prescribe appropriate treatments.


Histopatology images classification

The client stakeholders at Mayo Clinic were working closely with the team at DAC.digital

AI-driven Approach to Etiology Classification

DAC.digital

It was our first encounter with such large images. Some of these images were massive, reaching up to 2 GB in size. It was quite a challenge even to open them, let alone process, analyze, and evaluate them to determine the type of stroke. Handling these images required innovative approaches and techniques to ensure accurate classification.