Initial neural net construction for the detection of cervical intraepithelial neoplasia by fluorescence imaging.

Abstract

The aim of this study was to initiate neural net construction for the detection of cervical intraepithelial neoplasia by fluorescence imaging. Thirty-three women with abnormal Papanicolaou smears underwent fluorescence imaging during colposcopy. With the use of >4000 training pixels and >1000 test pixels, intrapatient nets were constructed from the spectral data of 17 women. An interpatient net that discriminated between cervical intraepithelial neoplasia 1 and normal tissue classes among patients was constructed with the use of >2300 training pixels and >2000 test pixels from 12 women. Average correct classification rates were determined. Sensitivities, specificities, and positive and negative predictive values for cervical intraepithelial neoplasia grade 1 and normal tissue classes were calculated. Extrapolated false-color cervical images were generated. Average correct classification rates were 96.5% for the intrapatient nets and 97.5% for the interpatient net. The sensitivity, specificity, and positive and negative predictive values for cervical intraepithelial neoplasia grade 1 were 98.2%, 98.9%, 71.4%, and 99.9%, respectively. Initial results suggest that neural nets that are constructed from fluorescence imaging spectra may offer a potential method for the detection of cervical intraepithelial neoplasia.