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

Authors
 Bio Joo  ;  Sung Soo Ahn  ;  Pyeong Ho Yoon  ;  Sohi Bae  ;  Beomseok Sohn  ;  Yong Eun Lee  ;  Jun Ho Bae  ;  Moo Sung Park  ;  Hyun Seok Choi  ;  Seung-Koo Lee 
Citation
 EUROPEAN RADIOLOGY, Vol.30(11) : 5785-5793, 2020-11 
Journal Title
EUROPEAN RADIOLOGY
ISSN
 0938-7994 
Issue Date
2020-11
MeSH
Algorithms* ; Deep Learning* ; Female ; Humans ; Intracranial Aneurysm / diagnosis* ; Magnetic Resonance Angiography / methods* ; Male ; Middle Aged ; ROC Curve ; Retrospective Studies
Keywords
Artificial intelligence ; Deep learning ; Intracranial aneurysm ; Magnetic resonance angiography
Abstract
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.
Full Text
https://link.springer.com/article/10.1007%2Fs00330-020-06966-8
DOI
10.1007/s00330-020-06966-8
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Sohn, Beomseok(손범석) ORCID logo https://orcid.org/0000-0002-6765-8056
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Joo, Bio(주비오)
Choi, Hyun Seok(최현석) ORCID logo https://orcid.org/0000-0003-4999-8513
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182678
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