PT  - JOURNAL ARTICLE
AU  - Lim, Hunjong
AU  - Choi, Dongjun
AU  - Sunwoo, Leonard
AU  - Jung, Jae Hyeop
AU  - Baik, Sung Hyun
AU  - Cho, Se Jin
AU  - Jang, Jinhee
AU  - Kim, Tackeun
AU  - Lee, Kyong Joon
TI  - Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study
AID  - 10.3174/ajnr.A8334
DP  - 2024 May 07
TA  - American Journal of Neuroradiology
PG  - ajnr.A8334
4099  - http://www.ajnr.org/content/early/2024/05/07/ajnr.A8334.short
4100  - http://www.ajnr.org/content/early/2024/05/07/ajnr.A8334.full
AB  - 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.