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題名 Automated breast imaging report generation based on the integration of multiple image features in a metadata format for shared decision-making
作者 羅崇銘
Lo, Chung-Ming;Chen, Hui-Ru
貢獻者 圖檔所
關鍵詞 computer-aided diagnosis; digital imaging and communications in medicine; image examination; metadata; reporting and data systems
日期 2024-08
上傳時間 25-十月-2024 09:39:47 (UTC+8)
摘要 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.
關聯 Health Informatics Journal, Vol.30, No.3, pp.1-14
資料類型 article
DOI https://doi.org/10.1177/14604582241288460
dc.contributor 圖檔所
dc.creator (作者) 羅崇銘
dc.creator (作者) Lo, Chung-Ming;Chen, Hui-Ru
dc.date (日期) 2024-08
dc.date.accessioned 25-十月-2024 09:39:47 (UTC+8)-
dc.date.available 25-十月-2024 09:39:47 (UTC+8)-
dc.date.issued (上傳時間) 25-十月-2024 09:39:47 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/154059-
dc.description.abstract (摘要) 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.
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Health Informatics Journal, Vol.30, No.3, pp.1-14
dc.subject (關鍵詞) computer-aided diagnosis; digital imaging and communications in medicine; image examination; metadata; reporting and data systems
dc.title (題名) Automated breast imaging report generation based on the integration of multiple image features in a metadata format for shared decision-making
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1177/14604582241288460
dc.doi.uri (DOI) https://doi.org/10.1177/14604582241288460