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Improved Turnaround Times | Median time to first decision: 12 days

Research ArticleORIGINAL RESEARCH

Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance 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 May 2024, ajnr.A8334; DOI: https://doi.org/10.3174/ajnr.A8334
Hunjong Lim
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Dongjun Choi
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Leonard Sunwoo
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Jae Hyeop Jung
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Sung Hyun Baik
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Se Jin Cho
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Jinhee Jang
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Tackeun Kim
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, Korea.
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Kyong Joon Lee
From the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Center for Artificial Intelligence in Healthcare (L.S.), Seoul National University Bundang Hospital, Seongnam, Korea; lululab Inc. (D.C.), Seoul, Korea; Remote Reading Team (J.H.J.), Korea Armed Forces Capital Hospital, Seongnam, Korea; Department of Radiology (J.J.), Seoul St. Mary’s Hospital, Seoul, Korea; TALOS Corp. (T.K.), Seoul, Korea; Monitor Corp. (K.J.L.), Seoul, 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-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries.

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

RESULTS: The average area under the Jackknife free-response receiver operating characteristic curve for the five radiologists demonstrated an improvement from 0.70 without artificial intelligence to 0.76 with artificial intelligence (P = .027). Notably, this improvement was most pronounced among the three radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time upon using artificial intelligence, there was no significant change among the readings by radiology residents. Moreover, the use of artificial intelligence resulted in improved inter-observer agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752).

CONCLUSIONS: Our proposed artificial intelligence 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; DL = Deep learning; ICC = Intraclass correlation coefficient; IRB = Institutional Review Boards; JAFROC = Jackknife free-response receiver operating characteristic.

Footnotes

  • The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dongjun Choi is currently an employee of lululab Inc., Seoul, Korea. Leonard Sunwoo is currently employed part-time at JLK Inc., Seoul, Korea. Tackeun Kim reports a relationship with TALOS Corp., Seoul, Korea, that includes: equity or stocks. Kyong Joon Lee reports a relationship with Monitor Corporation, Seoul, Korea, that includes: equity or stocks. Dongjun Choi, Leonard Sunwoo, Tackeun Kim, Kyong Joon Lee are listed as inventors on a patent related to the work. The patentee is Seoul National University Hospital, Seoul, Korea.

  • © 2024 by American Journal of Neuroradiology
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Accepted Manuscript
Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study
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 May 2024, ajnr.A8334; DOI: 10.3174/ajnr.A8334
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Cite this article
Accepted Manuscript
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 magnetic resonance angiography: an observer performance study
American Journal of Neuroradiology May 2024, ajnr.A8334; DOI: 10.3174/ajnr.A8334

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