Diagnosing human malformation patterns with a microcomputer: evaluation of two different algorithms.

Abstract

SYNDROC, a microcomputer-aided differential diagnostic approach to human malformation patterns, is based on a pseudo-Bayesian algorithm. This means that, for each sign, the frequency of this sign in the general population, its frequency in a particular syndrome, and the frequency of that particular syndrome have to be determined. These parameters are easy to find in common syndromes but tend to be difficult for rare or isolated cases. Thus, we implemented a new algorithm called the "descriptive algorithm," which defines a diagnosis by a set of anomalies all having the same weight. To test this algorithm, we analyzed 100 cases representing 100 different syndromes out of the register of the Division of Medical Genetics, Children's Hospital and Medical Center, University of Washington. The descriptive algorithm was allowed to give 3 sets of diagnoses. In 91% of the cases, this algorithm proposed the correct diagnosis (54% in the first window, 28% in the second window, and 9% in the third window). The number of diagnoses proposed was 18.78 +/- 16.57. The same cases were analyzed with the pseudo-Bayesian algorithm. The concordant diagnosis was proposed in 92% of the cases (55% at the top place, 11% at the second place, and 26% at the third place or beyond). The number of diagnoses submitted was 13.5 +/- 11.04. The combined algorithm gave the correct diagnosis in 96% of the cases. This study shows that the descriptive algorithm is as accurate as the pseudo-Bayesian algorithm in diagnosing malformation patterns, but this level is accompanied by an increased number of proposed diagnoses.