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<p>Improving mammography and ultrasound interpretation skills requires exposure to a large number of images. However, mammography images, particularly those stored in the Digital Imaging and Communications in Medicine (DICOM) format, are increasingly high-resolution and large, making them difficult to handle in personal computer (PC) environments.&#xa0;We developed a system that compresses mammography images while preserving quality and enables simultaneous learning with corresponding breast ultrasound images. Since 2022, this platform has been utilized by the Breast Cancer Screening Subcommittee for online learning to review the previous year’s screening images. Participants evaluated image quality on a 5-point scale. Using 2022 as a control, the Kruskal–Wallis test was used to compare image quality across 2023 and 2024. Participants also evaluated their perceived improvement (1–5 scale), and the Mann–Whitney test was applied to compare 2023 and 2024 data.&#xa0;File size was reduced to 3–13% of the original. Overall, 96% of participants were satisfied with image quality, and 75% of returning participants reported improved diagnostic skills. Although differences in image quality (<i>p</i> = 0.09) and diagnostic skill improvement (<i>p</i> = 0.167) were not significant, 98% expressed interest in continuing web-based training.&#xa0;This study is the first to report a system enabling screening professionals to review mammographic and ultrasound images they previously interpreted using a home PC. The findings suggest that a PC-based, remotely accessible e-learning environment offers greater convenience and comparable educational value to in-person workshops.</p>

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Development and Evaluation of A PC-Compatible E-Learning System for Mammography and Breast Ultrasound

  • 期刊论文

  • 发表日期：

    2025年12月16日

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  • Masako Sakuragi,
  • Mikio Shiozawa,
  • Satomi Shiba,
  • Yoshihide Nakamura,
  • Satoko Abe,
  • Hiroshi Morikubo,
  • Madoka Hasegawa,
  • Michiko Harao,
  • Joji Kitayama,
  • Naohiro Sata,
  • Hironori Yamaguchi,
  • Hiroshi Kawahira

摘要

Improving mammography and ultrasound interpretation skills requires exposure to a large number of images. However, mammography images, particularly those stored in the Digital Imaging and Communications in Medicine (DICOM) format, are increasingly high-resolution and large, making them difficult to handle in personal computer (PC) environments. We developed a system that compresses mammography images while preserving quality and enables simultaneous learning with corresponding breast ultrasound images. Since 2022, this platform has been utilized by the Breast Cancer Screening Subcommittee for online learning to review the previous year’s screening images. Participants evaluated image quality on a 5-point scale. Using 2022 as a control, the Kruskal–Wallis test was used to compare image quality across 2023 and 2024. Participants also evaluated their perceived improvement (1–5 scale), and the Mann–Whitney test was applied to compare 2023 and 2024 data. File size was reduced to 3–13% of the original. Overall, 96% of participants were satisfied with image quality, and 75% of returning participants reported improved diagnostic skills. Although differences in image quality (p = 0.09) and diagnostic skill improvement (p = 0.167) were not significant, 98% expressed interest in continuing web-based training. This study is the first to report a system enabling screening professionals to review mammographic and ultrasound images they previously interpreted using a home PC. The findings suggest that a PC-based, remotely accessible e-learning environment offers greater convenience and comparable educational value to in-person workshops.