Automated breast imaging report generation based on the integration of multiple image features in a metadata format for shared decision-making

Importance: Medical imaging increases the workload involved in writing reports. Given the lack of a standardized format for reports, reports are not easily used as communication tools. Objective: During medical team-patient communication, the descriptions in reports also need to be understood. Automatically generated imaging reports with rich and understandable information can improve medical quality. Design, setting, and participants: The image analysis theory of Panofsky and Shatford from the perspective of image metadata was used in this study to establish a medical image interpretation template (MIIT) for automated image report generation. Main outcomes and measures: The image information included digital imaging and communications in medicine (DICOM), reporting and data systems (RADSs), and image features used in computer-aided diagnosis (CAD). The utility of the images was evaluated by a questionnaire survey to determine whether the image content could be better understood. Results: In 100 responses, exploratory factor analysis revealed that the factor loadings of the facets were greater than 0.5, indicating construct validity, and the overall Cronbach's alpha was 0.916, indicating reliability. No significant differences were noted according to sex, age or education. Conclusions and relevance: Overall, the results show that MIIT is helpful for understanding the content of medical images.