Omar Bouhaddou

Decentration of Flexible Loop Posterior Chamber Intraocular Lenses in a Series of 222 Postmortem Eyes

Two hundred twenty-two postmortem eyes containing posterior chamber intraocular lenses (IOLs) were analyzed for optic decentration in relationship to lens style, implant duration, and loop fixation site. Decentration values were not affected significantly by either lens style or implant duration. In 33.3% of specimens, both loops were situated within the lens capsular sac, 18.0% had both loops fixated in the ciliary sulcus, and in 48.7% one loop was fixated in the lens capsular sac and the opposite loop in the ciliary sulcus or zonular region. There was a statistically significant difference in the amount of decentration in the three fixation groups studied. Capsular fixation provides the best and most consistent centration compared with fixation of both loops in the ciliary sulcus or asymmetrical fixation with only one loop in the capsular sac.

Evaluation of the Model

In the evaluation phase, the expert system is tested by comparing the differential diagnosis at each stage of the patient workup with the expert’s opinion and determining where the knowledge base might be revised to improve performance. This process is carried out first. If the system appears to be overconfident about a particular diagnosis (i.e., assigns a higher probability to that diagnosis than the expert thinks appropriate), suspicion is raised that the false positive rate assigned to some data item already entered for the patient being diagnosed is too low. Another explanation might be that highly associated findings are being treated as independent; i.e., they need to be represented as an “or” group or built into a cluster.

The Expert System Model

Medical informatics is the branch of science concerned with the use of computers and communication technology to acquire, store, analyze, communicate, and display medical information and knowledge to facilitate understanding and improve the accuracy, timeliness, and reliability of decision making.39 Understanding an observation is defined as the recognition of the relationship between what is observed and some prior observations or communication. A generalization that describes the relationships among such a set of observations is called a model of the set of observations or system. Decision making frequently involves the use of models. The subject matter in this volume is designed to assist the reader in learning to build a model of a system and implement the model using a computer so as to perform some useful intellectual task. Medical diagnosis is used as the principal example.

ILIAD: An Expert System for Diagnostic Assistance and Teaching: Implementation in France

Iliad is an expert system written in C for the Macintosh computer. The system operates in two modes: as an expert consultant to teach differential diagnosis and as knowledge-based patient case simulator to teach and test medical problem solving. An approach was proposed to create and maintain, in a syncronized fashion, a French version of the Iliad system. The French version is now being evaluated at the Faculte Lariboisiere Saint-Louis school of medicine and its evolution follows faithfully that of the master English version.

Example Knowledge Bases

The popularity of medical informatics has been on the upswing for a number of years as the role of computers has increased in importance in health care. The Department of Medical Informatics at the University of Utah has taught a graduate level course in Knowledge Engineering for the past 6 years.

Background and Legacy

The field of medical expert systems was reviewed by Reggia and Tuhrim in 1985,1 by P. Miller in 1988,2 and by R. Miller in 1994.3 Most of their observations, which are still true today, are summarized here.

The Data Dictionary: Limiting the Domain of the Model

The data dictionary of an expert system is the component that describes all the terms known to the system. In a diagnostic expert system, the data dictionary is the set of diseases and disease manifestations (i.e., symptoms, signs, laboratory results, radiological results, and special procedures) across all the diseases represented in the knowledge base.

Knowledge Engineering Tools

Knowledge engineering (KE) tools (i.e., software applications defined for a specific purpose) facilitate a quick cycle time from acquiring information from domain experts, the literature, and patient databases, to restructuring that information in the system and testing its performance against expert judgment and real cases. The process of developing a working knowledge base is iterative, and multiple cycles are necessary before a satisfactory behavior evolves.

Iliad: The Model Used for This Text

In the case of a diagnostic expert system, each disease or diagnostic decision can be represented as a list or table called a frame. The decision frame contains the relevant findings (i.e., disease manifestations), associated logic, and probabilities in the case of a probabilistic (e.g., Baye-sian) frame. Frames can be represented in different ways, e.g., Bayesian, Boolean, and value-type Boolean frames are used in the Iliad expert system.

The Knowledge Engineering Process

In a diagnostic medical expert system it is wise to make certain decisions before jumping into the details of designing individual frames. For example: For what purpose is the expert system being designed? Who will the primary users be? What are the limits of the medical domain to be modeled? This will affect the size and organization of the dictionary and data entry by the user. What is the patient population? The patient population will determine the a prioris and specificities to be used in the frames. How difficult it is to get the probability estimates? What experts are available? Approximately how much time and effort will be required to complete the initial construction of the knowledge base? Approximately how much time and effort will be required to test and evaluate the system, and what resources are available for the effort?