Literature DB >> 9293719

Knowledge-based system ADNEXPERT to assist the sonographic diagnosis of adnexal tumors.

J Brüning1, R Becker, M Entezami, V Loy, R Vonk, H Weitzel, T Tolxdorff.   

Abstract

ADNEXPERT is a knowledge-based system for the computer-assisted ultrasound diagnosis of adnexal tumors. In a case-based approach, ADNEXPERT used histopathologic and sonographic data from 2,290 adnexal tumors. After an ultrasound examination, the gynecologist interacts with the system. A maximum of 15 questions are posed; all but one question (age) relate to the sonographic findings. The help system gives online access to an ultrasound image library. Once the dialogue is complete, ADNEXPERT assesses the adnexal tumor pathology and makes a histological classification. A certainty factor (CF) model is used for knowledge representation. The CFs of the knowledge base are computed from the case database. During system evaluation, the accuracy of ADNEXPERT was tested by 69 new adnexal tumor cases, for which verified histopathological diagnoses were available. ADNEXPERT accurately assessed pathology in 49 cases (71%); in 10 cases (14%) correct indications to pathology were given; no diagnostic hints were attained in 2 cases (3%); and 8 cases (12%) were falsely diagnosed. Based on the positive results of the evaluation, ADNEXPERT will be tested under clinical conditions.

Entities:  

Mesh:

Year:  1997        PMID: 9293719

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  3 in total

1.  Ovarian tumor characterization and classification using ultrasound-a new online paradigm.

Authors:  U Rajendra Acharya; S Vinitha Sree; Luca Saba; Filippo Molinari; Stefano Guerriero; Jasjit S Suri
Journal:  J Digit Imaging       Date:  2013-06       Impact factor: 4.056

2.  A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study.

Authors:  Olivier Lucidarme; Jean-Paul Akakpo; Seth Granberg; Mario Sideri; Hanoch Levavi; Achim Schneider; Philippe Autier; Dror Nir; Harry Bleiberg
Journal:  Eur Radiol       Date:  2010-03-20       Impact factor: 5.315

3.  A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125.

Authors:  Valentina Chiappa; Matteo Interlenghi; Giorgio Bogani; Christian Salvatore; Francesca Bertolina; Giuseppe Sarpietro; Mauro Signorelli; Dominique Ronzulli; Isabella Castiglioni; Francesco Raspagliesi
Journal:  Eur Radiol Exp       Date:  2021-07-26
  3 in total

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