title: Application of neuron networks in the diagnostics of endometrial pathologies.
abstract: OBJECTIVE
  The aim of the study was to construct neuron networks utilizing selected risk factors and ultrasonographic (USG) examination parameters in a two-dimensional (2D) and three-dimensional (3D) presentation in relation to endometrial pathologies.
  
  
  METHODS
  The following risk factors were statistically analyzed: age and menopausal status, parity using hormonal replacement therapy (HRT), BMI, 2D USG of the endometrium (thickness, uterine artery blood flow indices) and 3D USG (volume, vascularization indices) in relation to the result of histopathological examination of the endometrial tissue in 421 women, aged 22-87 years, with abnormal bleeding from the uterus. The changes of the sensitivity and specificity in the applied models corresponding to changes of the limit value, were presented in the form of receiver operating characteristic curves (ROC) and the comparison of the values of the area under the curve (AUC). The threshold value for the obtained models was established and models of artificial neuron networks (ANN) were constructed on the basis of the ROC.
  
  
  CONCLUSIONS
  Application of artificial neural networks in medicine has been developing rapidly They have been applied in pre-surgical differentiation of ovarian tumors and other neoplasms. In case of endometrial carcinoma the degree of clinical usage of artificial neural networks has been limited, despite the fact that, from the mathematical point of view, the differentiation using neural networks would be much more precise than the one that could be obtained by chance.
authors.affiliation: University of Nevada, Las Vegas, USA.
authors.forename: Kotarska
authors.initials: K
authors.lastname: Maria
authors.forename: Smoleń
authors.initials: S
authors.lastname: Agata
authors.forename: Stachowicz
authors.initials: S
authors.lastname: Norbert
authors.forename: Kotarski
authors.initials: K
authors.lastname: Jan
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D004714:Endometrial Hyperplasia / Q000000981:diagnostic imaging; D016889:Endometrial Neoplasms / Q000000981:diagnostic imaging; D004717:Endometrium / Q000000981:diagnostic imaging* / Q000473:pathology*; D005260:Female; D005831:Genital Diseases, Female / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D014463:Ultrasonography; D014591:Uterine Diseases / Q000000981:diagnostic imaging; D055815:Young Adult