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Purpose: To construct a neural network model that can learn the differentdiagnosing strategies of radiologists to better classify aneurysm status inmagnetic resonance angiography images. Materials and methods: Thisretrospective study included 3423 time-of-flight brain magnetic resonanceangiography image series (subjects: male 1843 [mean age, 50.2 +/- 11.7 years],female 1580 [50.8 +/- 11.3 years]) recorded from November 2017 through January2019. The image series were read independently for aneurysm status by one offour board-certified radiologists, who were assisted by an established deeplearning-based computer-assisted diagnosis (CAD) system. The constructed neuralnetworks were trained to classify the aneurysm status of zero to fiveaneurysm-suspicious areas suggested by the CAD system for each image series,and any additional aneurysm areas added by the radiologists, and thisclassification was compared with the judgment of the annotating radiologist.Image series were randomly allocated to training and testing data in an 8:2ratio. The accuracy of the classification was compared by receiver operatingcharacteristic analysis between the control model that accepted only image dataas input and the proposed model that additionally accepted the information ofwho the annotating radiologist was. The DeLong test was used to compare areasunder the curves (P < 0.05 was considered significant). Results: The area underthe 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: Theproposed model improved classification accuracy by learning the diagnosisstrategies of individual annotating radiologists.

A neural network model that learns differences in diagnosis strategies  among radiologists has an improved area under the curve for aneurysm status  classification in magnetic resonance angiography image series