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: 8259048
• 负责人: CLAUDIA R MELLO-THOMS
• 金额: $ 11.56万
• 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 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8259048
• 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（由申请人提供）：乳腺癌是全世界女性中最常见的癌症，也是美国这一人群中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预后产生重大影响，并且向40岁以上的普通女性人群推荐乳房X线摄影筛查已经挽救了数千人的生命。然而，乳房X线照片的阅读是一项非常复杂的感知和认知任务，申请人先前已经表明，根据当前的培训指南，在其住院医师结束时，新手放射科医师在检测乳腺癌方面并不比乳腺技术人员更好，乳腺技术人员没有接受过任何关于如何检测这种疾病的正式培训。此外，这些放射科新手中的一小部分将成为多面手，也就是说，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光片。这些普通放射科医生的表现反映出缺乏乳腺成像方面的专门培训，与训练有素的乳腺放射科医生相比，他们不仅检测到较少的乳腺癌，而且检测到较少的早期乳腺病变。这个问题在非裔美国妇女中很严重，一般来说，由于社会经济地位，她们在社区中心或外展诊所接受乳房X光检查，这些设施往往由普通放射科医生组成。在这个项目中，我们建议建立一个以前设计和评估的基于计算机的辅导系统，名为SlideTutor，它已被开发为医学内的另一个视觉领域，即病理学的框架。在这一领域，使用SlideTutor已被证明在新手病理学家的诊断推理中产生了显着的改善，并且随着时间的推移，已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基于计算机的认知辅导系统，该系统可以教导通才和新手放射科医生（i）如何检测早期乳腺癌（从而提高整体灵敏度）;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，这是我们的假设，使用认知辅导。如果成功的话，这种干预措施将对向社会经济弱势群体提供医疗保健产生重大影响，并减少少数群体（如非洲裔美国人）与较富裕人口之间的医疗保健差距。此外，为了使尽可能多的放射科医生受益，我们的计算机辅导系统将放在因特网上，凡登记接受这方面培训的放射科医生均可免费使用。目前，乳房X光检查的漏诊率在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，以培训放射科医生如何发现乳腺癌的早期迹象。我们的系统将通过互联网部署，并将免费提供。