  Purpose: To construct a neural network model that can learn the different
diagnosing strategies of radiologists to better classify aneurysm status in
magnetic resonance angiography images. Materials and methods: This
retrospective study included 3423 time-of-flight brain magnetic resonance
angiography image series (subjects: male 1843 [mean age, 50.2 +/- 11.7 years],
female 1580 [50.8 +/- 11.3 years]) recorded from November 2017 through January
2019. The image series were read independently for aneurysm status by one of
four board-certified radiologists, who were assisted by an established deep
learning-based computer-assisted diagnosis (CAD) system. The constructed neural
networks were trained to classify the aneurysm status of zero to five
aneurysm-suspicious areas suggested by the CAD system for each image series,
and any additional aneurysm areas added by the radiologists, and this
classification was compared with the judgment of the annotating radiologist.
Image series were randomly allocated to training and testing data in an 8:2
ratio. The accuracy of the classification was compared by receiver operating
characteristic analysis between the control model that accepted only image data
as input and the proposed model that additionally accepted the information of
who the annotating radiologist was. The DeLong test was used to compare areas
under the curves (P < 0.05 was considered significant). Results: The area under
the curve was larger in the proposed model (0.845) than in the control model
(0.793), and the difference was significant (P < 0.0001). Conclusion: The
proposed model improved classification accuracy by learning the diagnosis
strategies of individual annotating radiologists.
