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

Abstract

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. 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. 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). 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.