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논문 기본 정보

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학술저널
저자정보
주비오 (강남세브란스병원) 최현석 (서울의료원) 안성수 (연세대학교) 차지훈 (연세대학교) 원소연 (연세대학교) 손범석 (연세대학교) 김휘영 (연세대학교) 한경화 (연세대학교) 김화평 (딥노이드(DEEPNOID)) 최종문 (딥노이드(DEEPNOID)) 이상민 (딥노이드(DEEPNOID)) 김태규 (딥노이드(DEEPNOID)) 이승구 (연세대학교)
저널정보
연세대학교 의과대학 Yonsei Medical Journal Yonsei Medical Journal 제62권 제11호
발행연도
2021.11
수록면
1,052 - 1,061 (10page)
DOI
10.3349/ymj.2021.62.11.1052

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Purpose: This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to that of human radiologists for approval from the Korean Ministry of Food and Drug Safety as an artificial intelligence-applied software. Materials and Methods: In this single-center, retrospective, confirmatory clinical trial, the diagnostic performance of the model was evaluated in a predetermined test set. After sample size estimation, the test set consisted of 135 aneurysm-containing examinations with 168 intracranial aneurysms and 197 aneurysm-free examinations. The target sensitivity and specificity were set as 87% and 92%, respectively. The patient-wise sensitivity and specificity of the model were analyzed. Moreover, the lesion-wise sensitivity and false-positive detection rate per case were also investigated. Results: The sensitivity and specificity of the model were 91.11% [95% confidence interval (CI): 84.99, 95.32] and 93.91% (95% CI: 89.60, 96.81), respectively, which met the target performance values. The lesion-wise sensitivity was 92.26%. The overall falsepositive detection rate per case was 0.123. Of the 168 aneurysms, 13 aneurysms from 12 examinations were missed by the model. Conclusion: The present deep learning model for automated detection of unruptured intracranial aneurysms on TOF MRA achieved the target diagnostic performance comparable to that of human radiologists. With high standalone performance, this model may be useful for accurate and efficient diagnosis of intracranial aneurysm.

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