Kundel H L, Nodine C F, Krupinski E A
Department of Radiology, University of Pennsylvania, Philadelphia 19104-6086.
Invest Radiol. 1990 Aug;25(8):890-6. doi: 10.1097/00004424-199008000-00004.
Approximately 30% of nodules are missed during the initial reading of chest radiographs. Eye-position recordings have shown that most nodules that are missed receive prolonged visual attention. A computer algorithm was developed that uses eye-position and gaze-duration times to identify locations on the chest image likely to contain missed nodules. These locations are highlighted on the displayed image to give visual feedback. The current study tested whether visual feedback was an effective aid to nodule detection. Six radiology residents searched 40 chest images for nodules while their eye-position and gaze-duration times were recorded. Half received displayed visual feedback and half were given a second view without feedback. Two months later the two groups returned and viewed the images in the opposite condition to counterbalance for possible practice effects. Performance of readers who were given feedback showed an average of 16% improvement as measured by the alternative free response operating characteristic (AFROC) curve area, A1. Performance of the same readers given a second look without feedback did not improve.
在初次阅读胸部X光片时，大约30%的结节会被漏诊。眼位记录显示，大多数被漏诊的结节都得到了较长时间的视觉关注。开发了一种计算机算法，该算法利用眼位和注视持续时间来识别胸部图像上可能包含漏诊结节的位置。这些位置在显示的图像上突出显示，以提供视觉反馈。当前的研究测试了视觉反馈是否是结节检测的有效辅助手段。六名放射科住院医师在搜索40张胸部图像中的结节时，他们的眼位和注视持续时间被记录下来。一半人收到显示的视觉反馈，另一半人则在没有反馈的情况下再次查看图像。两个月后，两组人员返回并以相反的条件查看图像，以平衡可能的练习效果。通过替代自由反应操作特征（AFROC）曲线面积A1测量，收到反馈的读者的表现平均提高了16%。在没有反馈的情况下再次查看图像的同一批读者的表现没有提高。
