Heathfield H, Bose D, Kirkham N
Information Technology Research Institute, Brighton Polytechnic, UK.
Pathol Res Pract. 1992 Jun;188(4-5):418-24. doi: 10.1016/S0344-0338(11)80030-0.
Accurate histological diagnosis of breast lesions is essential for the appropriate management of the patient. However, the technique of histological typing is problematic due to the large number of histological patterns, often of a complex and variable nature, which occur in breast disease. The introduction of the Breast Screening Programme has increased the burden on pathologists, and emphasised the need for training. Problems arise because mammographic screening detects a greater proportion of special histological types, with their attendant difficulties of identification, when compared to clinically palpable lesions. A computer-based decision support tool has been developed to assist pathologists in the histological diagnosis of breast disease. Unlike conventional expert systems, which seek to recreate the problem-solving processes of an expert, this system has been designed to act as an intelligent assistant to the pathologist. The system represents knowledge in the form of 'disease profiles', and utilises a novel inference model based upon the mathematical technique of hypergraphs. Initial trials with this system have demonstrated that a high level of diagnostic accuracy can be achieved.
对乳腺病变进行准确的组织学诊断对于患者的恰当治疗至关重要。然而，由于乳腺疾病中存在大量组织学模式，且通常具有复杂多变的性质，组织学分型技术存在问题。乳腺筛查计划的引入增加了病理学家的负担，并凸显了培训的必要性。出现问题的原因是，与临床可触及的病变相比，乳腺钼靶筛查检测出的特殊组织学类型比例更高，而这些类型在识别上存在相应困难。已开发出一种基于计算机的决策支持工具，以协助病理学家进行乳腺疾病的组织学诊断。与试图重现专家解决问题过程的传统专家系统不同，该系统被设计为病理学家的智能助手。该系统以“疾病概况”的形式呈现知识，并利用基于超图数学技术的新型推理模型。对该系统的初步试验表明，可以实现高水平的诊断准确性。
