Reducing false negatives in clinical practice: the role of neural network technology

Abstract

The fact that some cervical smears result in false-negative findings is an unavoidable and unpredictable consequence of the conventional (manual microscopic) method of screening. Errors in the detection and interpretation of abnormality are cited as leading causes of false-negative cytology findings; these are random errors that are not known to correlate with any patient risk factor, which makes the false-negative findings a "silent" threat that is difficult to prevent. Described by many as a labor-intensive procedure, the microscopic evaluation of a cervical smear involves a detailed search among hundreds of thousands of cells on each smear for a possible few that may indicate abnormality. Investigations into causes of false-negative findings preceding the discovery of high-grade lesions found that many smears had very few diagnostic cells that were often very small in size. These small cells were initially overlooked or misinterpreted and repeatedly missed on rescreening. PAPNET testing is designed to supplement conventional screening by detecting abnormal cells that initially may have been missed by microscopic examination. This interactive system uses neural networks, a type of artificial intelligence well suited for pattern recognition, to automate the arduous search for abnormality. The instrument focuses the review of suspicious cells by a trained cytologist. Clinical studies indicate that PAPNET testing is sensitive to abnormality typically missed by conventional screening and that its use as a supplemental test improves the accuracy of screening.