title: Cervical false negative cases detected by neural network-based technology. Critical review of cytologic errors.
abstract: OBJECTIVE
  To analyze the cytological errors made in the manual screening of cervical smears and to evaluate the usefulness of neural network-based technology (nnbt) in the detection of different kinds of errors.
  
  
  METHODS
  We reviewed 1,981 cervical smears by nnbt. Twelve false negatives (FNs) were detected and selected for study. The number of cell images showing atypical keratosis or atypical cells was evaluated on the monitor. The cellular features of the atypical cells (cellularity, cell type, nuclear and cytoplasmic changes, cellular arrangement and location on the slide) were analyzed by optic microscopy. Considering these qualitative and quantitative cytologic parameters and the diagnosis made by manual screening, we classified the errors into two groups: screening and interpretation related.
  
  
  RESULTS
  Four FNs were screening errors. Five FNs were classified as errors of interpretation. In three cases the cause of the cytologic errors could not be ascertained.
  
  
  CONCLUSIONS
  Our results confirm previous studies demonstrating that nnbt is useful for detecting screening errors. We also showed that it might be an adjunctive tool in the interpretation of abnormal cells, reducing the number of false negatives.
authors.affiliation: Cytopathology Laboratory, Department of Experimental Medicine and Pathology, La Sapienza University, Rome, Italy. mariarosaria.giovagnoli@uniroma1.it
authors.forename: Maria Rosaria
authors.initials: MR
authors.lastname: Giovagnoli
authors.forename: Maria
authors.initials: M
authors.lastname: Cenci
authors.forename: Salvatore Vincenzo
authors.initials: SV
authors.lastname: Olla
authors.forename: Aldo
authors.initials: A
authors.lastname: Vecchione
mesh_terms: D003951:Diagnostic Errors / Q000517:prevention & control*; D005188:False Negative Reactions; D005260:Female; D006801:Humans; D007642:Keratosis / Q000473:pathology; D008403:Mass Screening; D016571:Neural Networks, Computer; D011230:Precancerous Conditions / Q000473:pathology*; D002583:Uterine Cervical Neoplasms / Q000473:pathology*; D014626:Vaginal Smears