title: Less medical intervention after sharp demarcation of Grade 1-2 cervical intraepithelial neoplasia smears by neural network screening.
abstract: BACKGROUND
  Neural network technology has been used for the daily screening of cervical smears in The Netherlands since 1992. The authors believe this method might have the potential to demarcate diagnoses of Grade 1-2 cervical intraepithelial neoplasia (CIN 1-2).
  
  
  METHODS
  Of 133,196 women who were screened between 1992-1995, there were 2236 CIN 1-2 smears; 1128 of which were detected by means of neural network screening (NNS) (n = 83,404 women) and 1108 of which were diagnosed by conventional screening (n = 49,792 women). Cytologic and clinical outcomes (first cytologic or histologic follow-up diagnosis) were retrieved for all the women in the study population (n = 1920). Stratification based on clinical outcome resulted in the cases being grouped as overdiagnosed, concordant, or underdiagnosed. The smears were performed by general practitioners, whereas the biopsies were obtained by gynecologists.
  
  
  RESULTS
  The prevalence rate for CIN 1-2 was 1.15% (95% confidence interval [95% CI], 1.08-1.23%) for NNS and 1.92% (95% CI, 1.80-2.04%) for conventional diagnosis (P < 0.001). Concordance with histology was significantly higher for NNS (53.9%; 95% CI, 50.7-57.0%) compared with conventional screening (29.2%; 95% CI, 26.4-32.2%). In addition, overdiagnosis was significantly lower for cases diagnosed by NNS (39.4%; 95% CI, 36.3-42.4%) compared with cases diagnosed by conventional screening (62.4%; 95% CI, 59.3-65.5%).
  
  
  CONCLUSIONS
  Neural network-based screening can lead to fewer women being burdened unnecessarily with a cytologic diagnosis of CIN 1-2 by resulting in a sharp demarcation in these diagnoses and a corresponding reduction in unnecessary medical interventions. [See editorial on pages 171-172, this issue.]
authors.affiliation: Leiden Cytology and Pathology Laboratory, Leiden, The Netherlands.
authors.forename: M R
authors.initials: MR
authors.lastname: Kok
authors.forename: M E
authors.initials: ME
authors.lastname: Boon
authors.forename: P G
authors.initials: PG
authors.lastname: Schreiner-Kok
authors.forename: J
authors.initials: J
authors.lastname: Hermans
authors.forename: D E
authors.initials: DE
authors.lastname: Grobbee
authors.forename: L P
authors.initials: LP
authors.lastname: Kok
mesh_terms: D020732:Cytogenetic Analysis; D019468:Disease Management; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008403:Mass Screening / Q000379:methods; D016571:Neural Networks, Computer; D002583:Uterine Cervical Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000628:therapy; D014626:Vaginal Smears / Q000379:methods / Q000706:statistics & numerical data; D002578:Uterine Cervical Dysplasia / Q000145:classification / Q000175:diagnosis* / Q000628:therapy