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Deep Learning-Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA

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dc.contributor.author구자호-
dc.contributor.author박근영-
dc.contributor.author손범석-
dc.contributor.author이승구-
dc.contributor.author주비오-
dc.contributor.author차지훈-
dc.contributor.author최진교-
dc.contributor.author한경화-
dc.contributor.author원소연-
dc.contributor.author최현석-
dc.date.accessioned2022-02-23T01:23:25Z-
dc.date.available2022-02-23T01:23:25Z-
dc.date.issued2021-10-
dc.identifier.issn0195-6108-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/187673-
dc.description.abstractBackground and purpose: The detection of cerebral aneurysms on MRA is a challenging task. Recent studies have used deep learning-based software for automated detection of aneurysms on MRA and have reported high performance. The purpose of this study was to evaluate the incremental value of using deep learning-based software for the detection of aneurysms on MRA by 2 radiologists, a neurosurgeon, and a neurologist. Materials and methods: TOF-MRA examinations of intracranial aneurysms were retrospectively extracted. Four physicians interpreted the MRA blindly. After a washout period, they interpreted MRA again using the software. Sensitivity and specificity per patient, sensitivity per lesion, and the number of false-positives per case were measured. Diagnostic performances, including subgroup analysis of lesions, were compared. Logistic regression with a generalized estimating equation was used. Results: A total of 332 patients were evaluated; 135 patients had positive findings with 169 lesions. With software assistance, patient-based sensitivity was statistically improved after the washout period (73.5% versus 86.5%, P < .001). The neurosurgeon and neurologist showed a significant increase in patient-based sensitivity with software assistance (74.8% versus 85.2%, P = .03, and 56.3% versus 84.4%, P < .001, respectively), while the number of false-positive cases did not increase significantly (23 versus 30, P = .20, and 22 versus 24, P = .75, respectively). Conclusions: Software-aided reading showed significant incremental value in the sensitivity of clinicians in the detection of aneurysms on MRA without a significant increase in false-positive findings, especially for the neurosurgeon and neurologist. Software-aided reading showed equivocal value for the radiologist.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherAmerican Society of Neuroradiology-
dc.relation.isPartOfAMERICAN JOURNAL OF NEURORADIOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHBrain-
dc.subject.MESHDeep Learning*-
dc.subject.MESHHumans-
dc.subject.MESHIntracranial Aneurysm* / diagnostic imaging-
dc.subject.MESHMagnetic Resonance Angiography-
dc.subject.MESHRetrospective Studies-
dc.subject.MESHSensitivity and Specificity-
dc.subject.MESHSoftware-
dc.titleDeep Learning-Based Software Improves Clinicians' Detection Sensitivity of Aneurysms on Brain TOF-MRA-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiology (영상의학교실)-
dc.contributor.googleauthorB Sohn-
dc.contributor.googleauthorK-Y Park-
dc.contributor.googleauthorJ Choi-
dc.contributor.googleauthorJ H Koo-
dc.contributor.googleauthorK Han-
dc.contributor.googleauthorB Joo-
dc.contributor.googleauthorS Y Won-
dc.contributor.googleauthorJ Cha-
dc.contributor.googleauthorH S Choi-
dc.contributor.googleauthorS-K Lee-
dc.identifier.doi10.3174/ajnr.A7242-
dc.contributor.localIdA05781-
dc.contributor.localIdA01442-
dc.contributor.localIdA04960-
dc.contributor.localIdA02912-
dc.contributor.localIdA05842-
dc.contributor.localIdA05808-
dc.relation.journalcodeJ00095-
dc.identifier.eissn1936-959X-
dc.identifier.pmid34385143-
dc.identifier.urlhttp://www.ajnr.org/content/42/10/1769-
dc.contributor.alternativeNameKoo, Ja Ho-
dc.contributor.affiliatedAuthor구자호-
dc.contributor.affiliatedAuthor박근영-
dc.contributor.affiliatedAuthor손범석-
dc.contributor.affiliatedAuthor이승구-
dc.contributor.affiliatedAuthor주비오-
dc.contributor.affiliatedAuthor차지훈-
dc.citation.volume42-
dc.citation.number10-
dc.citation.startPage1769-
dc.citation.endPage1775-
dc.identifier.bibliographicCitationAMERICAN JOURNAL OF NEURORADIOLOGY, Vol.42(10) : 1769-1775, 2021-10-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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