Cervical false negative cases detected by neural network-based technology. Critical review of cytologic errors.
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.
Giovagnoli
Maria Rosaria
MR
Cytopathology Laboratory, Department of Experimental Medicine and Pathology, La Sapienza University, Rome, Italy. mariarosaria.giovagnoli@uniroma1.it
Cenci
Maria
M
Olla
Salvatore Vincenzo
SV
Vecchione
Aldo
A
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