Perednia D A, Gaines J A, Butruille T W
Department of Dermatology, School of Medicine, Oregon Health Sciences University, Portland.
Arch Dermatol. 1995 Mar;131(3):292-7.
Electronic medical imaging is important for medical informatics, computerized learning, and especially for the growing field of telemedicine. The image resolution necessary for a clinical application can be determined by use of receiver operating characteristic (ROC) experiments. Completely profiling display systems is a tedious process, requiring multiple ROC experiments. We have developed a multiple-choice ROC analysis technique to compare the relative informativeness of digital image formats for a spectrum of cutaneous lesions simultaneously. The technique makes use of logical competitor sets (LCSs) of clinical conditions to redefine multiple-choice responses into the present/absent framework required for conventional ROC curve construction. The study divided 180 slides and digital images into three LCSs: pigmented lesions, flesh-colored papules, and papulosquamous conditions. Eight dermatologists diagnosed the lesions presented in two randomized viewing sessions. Accuracy profiles, independent of individual observer sensitivities, were derived from the responses.
The informativeness of color slides and digital images was statistically similar, even when the conditions were stratified by difficulty of diagnosis. Results for nine specific skin conditions represented in the three LCSs were obtained simultaneously.
Digital images appear to be as informative as slides for specific dermatologic diagnoses in the three LCSs tested. The use of LCSs allows stratification of results by diagnosis with greater efficiency than multiple repeated ROC experiments. Multiple-choice ROC analysis used in conjunction with logical competitor sets is the best currently available method for comparing imaging media for use in visual disciplines such as dermatology, radiology, pathology, and others.
电子医学成像对于医学信息学、计算机化学习，尤其是对于不断发展的远程医疗领域而言至关重要。临床应用所需的图像分辨率可通过使用接受者操作特征（ROC）实验来确定。全面分析显示系统是一个繁琐的过程，需要进行多次ROC实验。我们开发了一种多项选择ROC分析技术，以同时比较一系列皮肤病变的数字图像格式的相对信息量。该技术利用临床状况的逻辑竞争集（LCS）将多项选择反应重新定义为传统ROC曲线构建所需的存在/不存在框架。该研究将180张幻灯片和数字图像分为三个LCS：色素沉着病变、肤色丘疹和丘疹鳞屑性疾病。八位皮肤科医生在两次随机观看环节中对呈现的病变进行诊断。从反应中得出独立于个体观察者敏感性的准确性概况。
即使按诊断难度对情况进行分层，彩色幻灯片和数字图像的信息量在统计学上也是相似的。同时获得了三个LCS中代表的九种特定皮肤状况的结果。
在测试的三个LCS中，对于特定的皮肤科诊断，数字图像似乎与幻灯片一样具有信息量。使用LCS可以比多次重复的ROC实验更有效地按诊断对结果进行分层。与逻辑竞争集结合使用的多项选择ROC分析是目前可用于比较皮肤病学、放射学、病理学等视觉学科中使用的成像介质的最佳方法。
