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

Authors
 B Sohn  ;  K-Y Park  ;  J Choi  ;  J H Koo  ;  K Han  ;  B Joo  ;  S Y Won  ;  J Cha  ;  H S Choi  ;  S-K Lee 
Citation
 AMERICAN JOURNAL OF NEURORADIOLOGY, Vol.42(10) : 1769-1775, 2021-10 
Journal Title
AMERICAN JOURNAL OF NEURORADIOLOGY
ISSN
 0195-6108 
Issue Date
2021-10
MeSH
Brain ; Deep Learning* ; Humans ; Intracranial Aneurysm* / diagnostic imaging ; Magnetic Resonance Angiography ; Retrospective Studies ; Sensitivity and Specificity ; Software
Abstract
Background 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.
Full Text
http://www.ajnr.org/content/42/10/1769
DOI
10.3174/ajnr.A7242
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
Yonsei Authors
Koo, Ja Ho(구자호)
Park, Keun Young(박근영)
Sohn, Beomseok(손범석) ORCID logo https://orcid.org/0000-0002-6765-8056
Won, So Yeon(원소연) ORCID logo https://orcid.org/0000-0003-0570-3365
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Joo, Bio(주비오) ORCID logo https://orcid.org/0000-0001-7460-1421
Cha, Jihoon(차지훈)
Choi, Jin Kyo(최진교)
Choi, Hyun Seok(최현석) ORCID logo https://orcid.org/0000-0003-4999-8513
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187673
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