Abstract：
In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale-for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed.
摘要：
在诊断医学中，患者的真实疾病状态通常以序数表示，例如，癌症阶段(0，I，II,III,andIV),使用CAD-RADs量表(无，最小，温和,中度,严重,并被遮挡）。随着诊断图像定量和人工智能(AI)的进步，正在开发监督和无监督算法，以帮助医生正确分级疾病。大多数诊断准确性文献涉及二元疾病状态（疾病存在或不存在）；然而，诊断序数疾病的测试评估不应仅仅为了简化诊断准确性测试而简化为二元状态.在本文中，作者提出了针对不同临床使用场景的序数尺度准确性的不同表征，以及比较测试的方法。在最简单的情况下，仅考虑正确等级的比例；其他方案解决了错误等级的大小和方向；在另一个极端，提出了基于不同类型错误等级的相对成本的加权精度度量。使用冠状动脉疾病示例说明了各种场景，其中比较了为患者提供正确CAD-RAD等级的AI算法的准确性。
