Computer expert system for the histopathologic diagnosis of salivary gland neoplasms

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.