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Tumour classification via ANN's

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  • Tumour classification via ANN's

    Dear Biomch-L readers,

    The following notice was distributed last month via the AIMED list. The
    principal author, David W. Piraino at the Cleveland Clinic Foundation will
    be out of email touch for the next three weeks. Fortunately, one of his
    colleagues at the CCF is a subscriber to Biomch-L and has kindly consented
    to act as a go-between for the time being.

    Persons interested in the notice below may get in touch with Brian L. Davis
    Ph.D., Dept. of Biomedical Engineering (Wb3), Cleveland Clinic Foundation
    at . Alternatively, Dr Piraino can be contacted at:
    Tel. +1(216)444-4845, Fax +1(216)445-9445.

    hjw

    --------------------------------

    Date: Fri, 18 Sep 92 11:32:50 EDT
    From: humphrey@nlm.nih.gov (Susanne M Humphrey)
    Sender: AI Medicine Digest [Volume 4, nr. 9]
    To: ai-medicine@medisg.Stanford.EDU

    The following file in archives:

    pub/ai-medicine/Biblio/Medline/92-05

    contains the citation:

    UI - 92126706
    EM - 9205
    AU - Piraino DW ; Amartur SC ; Richmond BJ ; Schils JP ; Thome JM ;
    Belhobek GH ; Schlucter MD
    TI - Application of an artificial neural network in radiographic
    diagnosis.
    AB - The description of 44 cases of bone tumors was used by an
    artificial neural network to rank the likelihood of 55 possible
    pathologic diagnoses. The performance of the artificial neural
    network was compared with the performance of experienced (3 or
    more years of radiology training) residents and inexperienced
    (less than 1 year of radiology training) residents. The
    artificial neural network was trained using descriptions of 110
    radiographs of bone tumors with known diagnoses. The descriptions
    of a separate set of 44 cases were used to test the neural
    network. The neural network ranked 55 possible pathologic
    diagnoses on a scale from 1 to 55. Experienced and inexperienced
    residents also ranked the possible diagnoses in the same 44
    cases. Inexperienced residents had a significantly lower mean
    proportion of diagnoses ranked first or second than did the
    neural network. Experienced residents had a significantly higher
    proportion of correct diagnoses ranked first than did the
    neural network. Experienced residents had a significantly higher
    proportion of correct diagnoses ranked first than did the
    network. Otherwise, a significant difference between the
    performance of the network and experienced or inexperienced
    residents was not identified. These results demonstrate that
    artificial neural networks can be trained to classify bone
    tumors. Whether neural network performance in classification of
    bone tumors can be made accurate enough to assist radiologists in
    clinical practice remains an open question. These preliminary
    results indicate that further investigation of this technology
    for interpretation assistance is warranted.
    MH - Bone Neoplasms/RADIOGRAPHY ; Human ; *Neural Networks (Computer)
    ; *Radiographic Image Interpretation, Computer-Assisted
    AD - Department of Radiology, Cleveland Clinic Foundation, OH
    44195-5021.
    SO - J Digit Imaging 1991 Nov;4(4):226-32

    I do not know if this article publishes the actual algorithm, but the
    author address is given.
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