A Web-based multi-agent system for interpreting medical images

  • Authors:

  • Yi Shang;Hongchi Shi

  • Affiliations:

  • Department of Computer Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA E-mail: {shangy,shih}@missouri.edu;Department of Computer Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA E-mail: {shangy,shih}@missouri.edu

  • Venue:
  • World Wide Web
  • Year:
  • 1999

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Abstract

A difficult problem in medical image interpretation is that for every image type such as x-ray and every body organ such as heart, there exist specific solutions that do not allow for generalization. Just collecting all the specific solutions will not achieve the vision of a computerized physician. To address this problem, we develop an intelligent agent approach based on the concept of active fusion and agent-oriented programming. The advantage of agent-oriented programming is that it combines the benefits of object-oriented programming and expert system. Following this approach, we develop a Web-based multi-agent system for interpreting medical images. The system is composed of two major types of intelligent agents: radiologist agents and patient representative agents. A radiologist agent decomposes the image interpretation task into smaller subtasks, uses multiple agents to solve the subtasks, and combines the solutions to the subtasks intelligently to solve the image interpretation problem. A patient representative agent takes questions from the user (usually a patient) through a Web-based interface, asks for multiple opinions from radiologist agents in interpreting a given set of images, and then integrates the opinions for the user. In addition, a patient representative agent can answer questions based on the information in a medical information database. To maximize the satisfaction that patients receive, the patient representative agents must be as informative and timely as communicating with a human. With an efficient pseudo-natural language processing, a knowledge base in XML, and user communication through Microsoft Agent, the patient representative agents can answer questions effectively.