# Neurologists-level interpretable CT-based deep neural network for prediction of hemorrhagic transformation after ischemic stroke

## TL;DR

This paper presents a deep learning model using CT scans to predict hemorrhagic transformation after stroke, achieving performance comparable to neurologists.

## Contribution

A novel interpretable deep learning model for HT prediction in ischemic stroke patients, outperforming existing models and clinicians.

## Key findings

- The model achieved an F1 score of 78.94% and an AUC of 0.842 in predicting hemorrhagic transformation.
- The model demonstrated higher accuracy and sensitivity compared to clinical physicians and existing models.
- The model uses plain CT scans and residual networks for prediction, offering clinical interpretability.

## Abstract

Hemorrhagic transformation (HT) is a severe complication following acute ischemic stroke, associated with neurological deterioration and poor clinical outcomes. Deep learning represents a promising tool for HT prediction.

We conducted a retrospective analysis of 474 acute ischemic stroke cases (231 HT and 243 non-HT) admitted to Beijing Tiantan Hospital from April 2014 to November 2022. We constructed a dataset from this cohort and randomly partitioned it into training and validation sets. Subsequently, we developed a model utilizing convolutional neural networks (CNNs) and residual networks based on computed tomography (CT) scans to predict HT after ischemic stroke.

The final dataset consisted of 613 CT scans. The model achieved an F1 score of 78.94% (95% CI, 67.7–86.4). The Area Under the Curve (AUC) was 0.842 (95% CI, 75.8–92.1), sensitivity was 71.55% (95% CI, 60.6%−85.0%), and accuracy was 74.52% (95% CI, 63.9%−83.2%).

By combining plain CT scans with deep learning methodologies, we developed a clinically applicable model with demonstrable interpretability. Primarily designed to predict HT after acute ischemic stroke, this model demonstrated significant performance advantages in testing compared to both clinical physicians and similar existing models.