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Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Jiehua Yang¹, Mingfei Xie¹, Canpei Hu¹

¹From the School of Electronic Information and Communications, Huazhong University of Science and Technology, South 1st Building, Luoyu Road 1037, Wuhan 430074, China (J.Y., C.H., Y.X.); Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (M.X., O.A., J.L., T.J., X. Long); Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China (M.X., O.A., J.L., T.J., X. Long); Department of Radiology, Xin Cai People's Hospital, Xin Cai, China (Changde Li); and Huawei Technologies, Shenzhen, China (D.T., X. Liu, C.Z., Cixing Li).

|November 3, 2020

Summary

A new deep learning algorithm significantly improves cerebral aneurysm detection on CT angiography scans. This AI tool aids radiologists, increasing detection rates and identifying previously missed aneurysms.

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Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence in Radiology
Neurosurgery

Background:

Cerebral aneurysm detection is a complex diagnostic challenge.
Deep learning (DL) offers potential for enhancing interpretation accuracy in medical imaging.
Accurate detection of cerebral aneurysms is critical for preventing rupture and improving patient outcomes.

Purpose of the Study:

To develop and validate a highly sensitive deep learning-based algorithm for detecting cerebral aneurysms.
To assess the algorithm's performance in assisting radiologists with CT angiography interpretation.
To improve the diagnostic accuracy and detection rates of cerebral aneurysms.

Main Methods:

Retrospective retrieval of head CT angiography images from two hospital databases (January 2015 - June 2019).
Development and internal validation of a DL algorithm using training and validation sets.
External validation using 400 independent CT angiograms; analysis via Jackknife alternative free-response receiver operating characteristic (AFROC) methodology.

Main Results:

The DL algorithm achieved a sensitivity of 97.5% for detecting cerebral aneurysms (633/649).
The algorithm identified eight previously overlooked aneurysms (1.2% of cases).
Radiologist performance, measured by the area under the weighted AFROC curve, improved by 0.01 with AI assistance.

Conclusions:

The developed deep learning algorithm effectively assists radiologists in detecting cerebral aneurysms on CT angiography.
The AI tool demonstrated high sensitivity and contributed to a higher overall detection rate.
This technology shows promise for enhancing the accuracy and efficiency of cerebral aneurysm diagnosis.