A deep learning algorithm may automate intracranial aneurysm detection on MR angiography with high diagnostic performance

• Published in: European radiology Vol. 30; no. 11; pp. 5785 - 5793
• Main Authors: Joo, Bio, Ahn, Sung Soo, Yoon, Pyeong Ho, Bae, Sohi, Sohn, Beomseok, Lee, Yong Eun, Bae, Jun Ho, Park, Moo Sung, Choi, Hyun Seok, Lee, Seung-Koo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
• Subjects: Algorithms; Deep Learning; Diagnostic Radiology; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Intracranial Aneurysm - diagnosis; Magnetic Resonance; Magnetic Resonance Angiography - methods; Male; Medicine; Medicine & Public Health; Middle Aged; Neuroradiology; Radiology; Retrospective Studies; ROC Curve; Ultrasound; Deep learning; Artificial intelligence; Intracranial aneurysm; Magnetic resonance angiography
• ISSN: 0938-7994; 1432-1084; 1432-1084
• DOI: 10.1007/s00330-020-06966-8

Objectives To develop a deep learning algorithm for automated detection and localization of intracranial aneurysms on time-of-flight MR angiography and evaluate its diagnostic performance. Methods In a retrospective and multicenter study, MR images with aneurysms based on radiological reports were extracted. The examinations were randomly divided into two data sets: training set of 468 examinations and internal test set of 120 examinations. Additionally, 50 examinations without aneurysms were randomly selected and added to the internal test set. External test data set consisted of 56 examinations with intracranial aneurysms and 50 examinations without aneurysms, which were extracted based on radiological reports from a different institution. After manual ground truth segmentation of aneurysms, a deep learning algorithm based on 3D ResNet architecture was established with the training set. Its sensitivity, positive predictive value, and specificity were evaluated in the internal and external test sets. Results MR images included 551 aneurysms (mean diameter, 4.17 ± 2.49 mm) in the training, 147 aneurysms (mean diameter, 3.98 ± 2.11 mm) in the internal test, 63 aneurysms (mean diameter, 3.23 ± 1.69 mm) in the external test sets. The sensitivity, the positive predictive value, and the specificity were 87.1%, 92.8%, and 92.0% for the internal test set and 85.7%, 91.5%, and 98.0% for the external test set, respectively. Conclusion A deep learning algorithm detected intracranial aneurysms with a high diagnostic performance which was validated using external data set. Key Points • A deep learning-based algorithm for the automated diagnosis of intracranial aneurysms demonstrated a high sensitivity, positive predictive value, and specificity. • The high diagnostic performance of the algorithm was validated using external test data set from a different institution with a different scanner. • The algorithm might be robust and effective for general use in real clinical settings.