MammoTutor: An Internet-Based Computer Tutoring System to Teach General Radiol

MammoTutor：基于互联网的计算机辅助系统，用于教授一般放射学

• 批准号: 8072153
• 负责人: CLAUDIA R MELLO-THOMS
• 金额: $ 10.95万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8072153
• 关键词: MammoTutor; Internet; Based; Computer; Tutoring

DESCRIPTION (Provided by Applicant): Breast cancer is the most common cancer among women worldwide, and the second leading cause of cancer-related deaths among this population in the United States. As with many other types of cancer, early detection can have a significant impact in patient prognosis, and the recommendation of mammography screening to the general women population older than 40 years of age has saved thousands of lives. However, the reading of a mammogram is a very complex perceptual and cognitive task, and the applicant has previously shown that, under the current training guidelines, at the end of their residency, novice radiologists are no better at detecting breast cancer than breast technologists, who do not receive any formal training in how to detect this disease. Moreover, a fraction of these novice radiologists will become generalists, namely, they will be board certified to read a lot of different images, including mammograms. The lack of specialized training in breast imaging is reflected in the performance of these general radiologists, who not only detect fewer breast cancers but also detect fewer early-staged breast lesions when compared to better trained breast radiologists. This problem is acute amongst African-American women, who, in general, due to socio-economical status, receive their mammograms at community-based centers or at outreach clinics, facilities that tend to be staffed by general radiologists. In this project we propose to build upon the framework of a previously designed and evaluated computer-based tutoring system, named SlideTutor, which has been developed for another visual domain within Medicine, namely, Pathology. In that domain, use of SlideTutor has been shown to produce significant improvements in diagnostic reasoning among novice pathologists, and retention of the learned material has been observed over time. Thus, it is our goal to use a similar framework to develop a computer-based cognitive tutoring system that can teach generalists and novice radiologists (i) how to detect early-staged breast cancer (thus yielding improvements in overall sensitivity); and (ii) how to reduce the number of unnecessary biopsy recommendations (therefore improving specificity). Hence, it is our hypothesis that use of cognitive tutoring will. If successful, such intervention should have a significant impact in health care delivery to socio-economically disadvantage populations, and to reduce health care disparities between minority populations (such as African-Americans) and more affluent populations. In addition, in order to reach as wide a number of radiologists as possible, our computer-based tutoring system will be placed on the Internet, where it will be available, free of charge, to any radiologists who register to receive training in this task. Currently, the missed rates at mammography screenings are between 10 and 30 percent. In this project, we will develop a computer based tutoring system to train radiologists how to detect early signs of breast cancer. Our system will be deployed over the internet and will be available free of charge.

描述(申请人提供)：乳腺癌是全世界女性中最常见的癌症，也是美国女性癌症相关死亡的第二大原因。与许多其他类型的癌症一样，早期发现可以对患者的预后产生重大影响，向40岁以上的普通女性人群推荐乳房X光检查已经挽救了数千人的生命。然而，读取乳房X光照片是一项非常复杂的感知和认知任务，申请者以前曾表明，根据目前的培训指南，在实习结束时，新手放射科医生在检测乳腺癌方面并不比乳房技术员强，后者没有接受如何检测这种疾病的任何正式培训。此外，这些新手放射科医生中的一小部分将成为多面手，即他们将获得委员会认证，可以阅读许多不同的图像，包括乳房X光照片。这些普通放射科医生的表现反映出缺乏乳房成像方面的专门培训，与训练有素的乳房放射科医生相比，他们不仅发现的乳腺癌较少，而且发现的早期乳房病变也较少。这一问题在非洲裔美国女性中尤为严重，由于社会经济地位的原因，她们一般在社区中心或外展诊所接受乳房X光检查，这些机构往往配备普通放射科医生。在这个项目中，我们建议建立在先前设计和评估的基于计算机的教学系统SlideTutor的框架上，该系统是为医学中的另一个视觉领域，即病理学开发的。在这一领域，幻灯片导师的使用已经被证明在新手病理学家中的诊断推理方面产生了显著的改进，并且随着时间的推移，已经观察到学习材料的保留。因此，我们的目标是使用类似的框架来开发一种基于计算机的认知辅导系统，该系统可以教授多面手和放射科新手(I)如何检测早期乳腺癌(从而提高总体敏感度)；以及(Ii)如何减少不必要的活检建议的数量(从而提高特异性)。因此，我们的假设是，使用认知辅导将会。如果成功，这种干预措施将对向社会经济上处于不利地位的人群提供保健服务产生重大影响，并缩小少数群体(如非裔美国人)与较富裕人群之间的保健差距。此外，为了尽可能广泛地接触到更多的放射科医生，我们的电脑辅导系统将放在互联网上，任何注册接受这项任务培训的放射科医生都可以免费使用。目前，乳房X光检查的漏检率在10%到30%之间。在这个项目中，我们将开发一个基于计算机的辅导系统，培训放射科医生如何检测乳腺癌的早期症状。我们的系统将部署在互联网上，并将免费提供。