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

• URL: http://arxiv.org/abs/2002.01891v1
• Date: Mon, 3 Feb 2020 19:19:57 GMT
• Title: 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
• Authors: Yasuhiko Tachibana, Masataka Nishimori, Naoyuki Kitamura, Kensuke Umehara, Junko Ota, Takayuki Obata, and Tatsuya Higashi
• Abstract summary: This retrospective study included 3423 time-of-flight brain magnetic resonance angiography image series. The image series were read independently for aneurysm status by one of four board-certified radiologists. The constructed neural networks were trained to classify the aneurysm status of zero to five aneurysm-suspicious areas.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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