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Research ArticleArtificial Intelligence

Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study

Hunjong Lim, Dongjun Choi, Leonard Sunwoo, Jae Hyeop Jung, Sung Hyun Baik, Se Jin Cho, Jinhee Jang, Tackeun Kim and Kyong Joon Lee
American Journal of Neuroradiology September 2024, 45 (9) 1253-1259; DOI: https://doi.org/10.3174/ajnr.A8334
Hunjong Lim
aFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea
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Dongjun Choi
blululab Inc. (D.C.), Seoul, South Korea
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Leonard Sunwoo
aFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea
cCenter for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, South Korea
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Jae Hyeop Jung
aFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea
dRemote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, South Korea
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Sung Hyun Baik
aFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea
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Se Jin Cho
aFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea
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Jinhee Jang
eDepartment of Radiology (J.J.), Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
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Tackeun Kim
fTALOS Corp. (T.K.), Seoul, South Korea
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Kyong Joon Lee
aFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea
gMonitor Corp. (K.J.L.), Seoul, South Korea
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Abstract

BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries.

MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from 2 institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by 5 radiologists (2 neuroradiologists and 3 radiology residents) to compare the usage and nonusage of our proposed AI model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed by using the area under the jackknife free-response receiver operating characteristic curve (AUFROC) and reading time for comparison.

RESULTS: The average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI (P = .027). Notably, this improvement was most pronounced among the 3 radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time by using AI, there was no significant change among the readings by radiology residents. Moreover, the use of AI resulted in improved interobserver agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).

CONCLUSIONS: Our proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.

ABBREVIATIONS:

AI
artificial intelligence
AUC
area under the receiver operating characteristic curve
AUFROC
area under the jackknife free-response receiver operating characteristic curve
ICC
intraclass correlation coefficient
JAFROC
jackknife free-response receiver operating characteristic
  • © 2024 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 45 (9)
American Journal of Neuroradiology
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Cite this article
Hunjong Lim, Dongjun Choi, Leonard Sunwoo, Jae Hyeop Jung, Sung Hyun Baik, Se Jin Cho, Jinhee Jang, Tackeun Kim, Kyong Joon Lee
Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study
American Journal of Neuroradiology Sep 2024, 45 (9) 1253-1259; DOI: 10.3174/ajnr.A8334

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Automated Detection of MR Angiography Lesions
Hunjong Lim, Dongjun Choi, Leonard Sunwoo, Jae Hyeop Jung, Sung Hyun Baik, Se Jin Cho, Jinhee Jang, Tackeun Kim, Kyong Joon Lee
American Journal of Neuroradiology Sep 2024, 45 (9) 1253-1259; DOI: 10.3174/ajnr.A8334
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