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SYNDROC: microcomputer based differential diagnosis of malformation patterns.

D Schorderet, P Aebischer

    Archives of Disease in Childhood
    |March 1, 1985
    PubMed
    Summary

    Diagnosing rare human malformation patterns is challenging. A new microcomputer program, SYNDROC, aids in differential diagnosis of dysmorphic syndromes with 95% accuracy, improving clinical decision-making.

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    Area of Science:

    • Medical Genetics
    • Clinical Dysmorphology
    • Computational Biology

    Background:

    • Differential diagnosis of human malformation patterns is complex due to the large number of syndromes and their low incidence.
    • Clinical experience is often insufficient for accurate diagnosis of rare dysmorphic syndromes.

    Purpose of the Study:

    • To develop and evaluate a microcomputer-based program for the differential diagnosis of dysmorphic syndromes.
    • To assist clinicians in diagnosing rare malformation patterns by compensating for experience limitations.

    Main Methods:

    • Development of the SYNDROC program using PL/I-80 and an MDBS III database with over 700 signs and 300 syndromes.
    • Implementation of a three-tiered diagnostic algorithm: pseudo-Bayesian initial diagnosis, heuristic refinement, and Boolean retrieval for undiagnosed cases.
    • Retrospective validation using 100 dysmorphic cases.

    Main Results:

    • The SYNDROC program demonstrated high reliability in differential diagnosis of dysmorphic syndromes.
    • A 95% agreement was observed between the program's top two suggested diagnoses and those made by experienced geneticists.
    • The system facilitates the recognition of new dysmorphic disorders through storage and retrieval of undiagnosed cases.

    Conclusions:

    • The SYNDROC program is a valuable tool for the differential diagnosis of dysmorphic syndromes, enhancing diagnostic accuracy.
    • Computer-aided diagnosis can significantly support clinical geneticists in managing complex malformation patterns.
    • The SYNDROC system shows potential for identifying novel genetic disorders through its data management capabilities.

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