Deep-Learning Tool for Early Identifying Non-Traumatic Intracranial Hemorrhage Etiology based on CT Scan

by   Meng Zhao, et al.

Background: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology based on non-contrast CT (NCCT) scans and investigate whether clinicians can benefit from it in a diagnostic setting. Materials and Methods: The deep learning model was developed with 1868 eligible NCCT scans with non-traumatic ICH collected between January 2011 and April 2018. We tested the model on two independent datasets (TT200 and SD 98) collected after April 2018. The model's diagnostic performance was compared with clinicians's performance. We further designed a simulated study to compare the clinicians's performance with and without the deep learning system augmentation. Results: The proposed deep learning system achieved area under the receiver operating curve of 0.986 (95 on aneurysms, 0.952 (0.917-0.987) on hypertensive hemorrhage, 0.950 (0.860-1.000) on arteriovenous malformation (AVM), 0.749 (0.586-0.912) on Moyamoya disease (MMD), 0.837 (0.704-0.969) on cavernous malformation (CM), and 0.839 (0.722-0.959) on other causes in TT200 dataset. Given a 90 level, the sensitivities of our model were 97.1 diagnosis, respectively. The model also shows an impressive generalizability in an independent dataset SD98. The clinicians achieve significant improvements in the sensitivity, specificity, and accuracy of diagnoses of certain hemorrhage etiologies with proposed system augmentation. Conclusions: The proposed deep learning algorithms can be an effective tool for early identification of hemorrhage etiologies based on NCCT scans. It may also provide more information for clinicians for triage and further imaging examination selection.


page 20

page 29

page 30

page 35


Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans

Importance: Non-contrast head CT scan is the current standard for initia...

Identification of Hemorrhage and Infarct Lesions on Brain CT Images using Deep Learning

Head Non-contrast computed tomography (NCCT) scan remain the preferred p...

Deep Learning for Segmentation-based Hepatic Steatosis Detection on Open Data: A Multicenter International Validation Study

Despite high global prevalence of hepatic steatosis, no automated diagno...

An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph

Recent artificial intelligence (AI) algorithms have achieved radiologist...

DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning

We propose a deep learning-based technique for detection and quantificat...

Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans

We propose developing and validating a three-dimensional (3D) deep learn...

Please sign up or login with your details

Forgot password? Click here to reset