Firriolo F J, Levy B A
Department of Diagnosis and General Dentistry, University of Louisville, School of Dentistry, Ky., USA.
Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 1996 Aug;82(2):179-86. doi: 10.1016/s1079-2104(96)80222-8.
The design, development, and testing of a prototype interactive histopathologic expert system capable of diagnosing 15 types of primary salivary gland neoplasms is described. The system incorporates a multiple subprogram modular design and makes use of multiple reasoning methods including: data-driven and goal-directed rule-based reasoning, linear pattern recognition, and Bayesian classification. Its user interface incorporates both a "hypertext" context-sensitive information assistance facility and the video display of stored and digitized photomicrographic images. The system can report a differential diagnosis of its findings with assessment of its confidence in its diagnosis. The system's performance was evaluated in a series of tests. The results of a weighted kappa analysis of the system's diagnoses versus those of four oral pathologists for 20 salivary gland neoplasms indicated no statistical difference in diagnostic performance between the system and the human experts and each of the experts in relationship to the others (Wilcoxon rank sums test). A modified version of Turing's test of artificial intelligence demonstrated no statistically significant difference in the system's diagnoses versus the diagnosis of four human expert pathologists (Fisher's exact test). The knowledge and experience gained in the development and testing of the expert system described in this study have demonstrated the validity of histopathologic diagnostic expert systems in a selected area of oral pathology.
本文描述了一个能够诊断15种原发性涎腺肿瘤的交互式组织病理学专家系统原型的设计、开发和测试。该系统采用多子程序模块化设计，并运用多种推理方法，包括：数据驱动和目标导向的基于规则的推理、线性模式识别和贝叶斯分类。其用户界面包含一个“超文本”上下文敏感信息辅助工具以及存储和数字化的显微照片的视频显示。该系统可以报告其诊断结果的鉴别诊断，并评估其对诊断的置信度。该系统的性能在一系列测试中进行了评估。对20例涎腺肿瘤，该系统的诊断结果与四位口腔病理学家的诊断结果进行加权kappa分析，结果表明该系统与人类专家以及各位专家之间在诊断性能上无统计学差异（Wilcoxon秩和检验）。人工智能的图灵测试的一个修改版本表明，该系统的诊断结果与四位人类专家病理学家的诊断结果之间无统计学显著差异（Fisher精确检验）。本研究中所述专家系统的开发和测试所获得的知识和经验证明了组织病理学诊断专家系统在口腔病理学特定领域的有效性。
