Consequences of neural network technology for cervical screening: increase in diagnostic consistency and positive scores

Cancer. 1996 Jul 1;78(1):112-7. doi: 10.1002/(SICI)1097-0142(19960701)78:1<112::AID-CNCR16>3.0.CO;2-2.


Background: Screening programs for the early detection of cervical carcinoma are criticized because of the problem of false-negative diagnoses. A successful approach for solving this problem is applying neural network technology (PAP-NET) to assist the cytotechnologist (CT) in finding the (few) abnormal cells in the smear.

Methods: In 3 consecutive years (1992, 1993, and 1994), 25,767 smears were screened conventionally and 65,527 with the aid of PAPNET by 7 CTs. For each CT, the scores for atypias of undetermined significance, squamous or glandular (ASCUC/AGUS according to the Bethesda classification system), indicated by Positive 1, for low grade precursor lesions, by Positive II, for high grade lesions and invasive carcinoma, by Positive III, were calculated for both screening methods. The histologic scores were also calculated.

Results: The mean positive scores of the seven CTs were higher for PAPNET than for conventional screening, and the coefficients of variability were lower. For Positive III smears, the consistency in screening was significantly higher for PAPNET than for conventional screening. The higher histologically positive scores for carcinoma in situ and invasive carcinoma indicated an increased screening sensitivity.

Conclusions: As demonstrated by the improvement in the performances of all CTs involved, screening efficacy was enhanced by the use of neural network technology.

Publication types

  • Clinical Trial
  • Randomized Controlled Trial

MeSH terms

  • Adult
  • Carcinoma / pathology
  • Carcinoma in Situ / pathology
  • Female
  • Humans
  • Middle Aged
  • Neural Networks, Computer*
  • Sensitivity and Specificity
  • Uterine Cervical Dysplasia / pathology
  • Uterine Cervical Neoplasms / classification
  • Uterine Cervical Neoplasms / pathology*
  • Vaginal Smears* / classification