Numerous ancillary techniques detecting HPV DNA or mRNA are viewed as competitors or ancillary techniques to test Papanicolaou. However, no technique is perfect because sensitivity increases at the cost of specificity. Various methods have been applied to resolve this issue by using many examination results, such as classification and regression trees and supervised artificial neural networks. In this article, 1258 cases with results from test Pap, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of the self-organizing map (SOM). An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps. The results of the SOM application were encouraging and produced maps depicting the important tests.
The COVID-19 pandemic has challenged health systems worldwide by decreasing their reserves and effectiveness. In this changing landscape, the urge for reallocation of financial and human resources represents a top priority. In screening, effectiveness and efficiency are most relevant. In the quest against cervical cancer, numerous molecular ancillary techniques detecting HPV DNA or mRNA or other related biomarkers complement morphological assessment by the Papanicolaou test. However, no technique is perfect as sensitivity increases at the cost of specificity. Various approaches try to resolve this issue by incorporating several examination results, such as artificial intelligence are proposed. In this study, 1,258 cases with a complete result dataset for cytology, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of a self-organizing map (SOM), an unsupervised artificial neural network. The results of the SOM application were encouraging since it is capable of producing maps discriminating the necessary tests and has improved performance.
Numerous ancillary techniques detecting HPV DNA or mRNA are viewed as competitors or ancillary techniques to test Papanicolaou. However, no technique is perfect because sensitivity increases at the cost of specificity. Various methods have been applied to resolve this issue by using many examination results, such as classification and regression trees and supervised artificial neural networks. In this article, 1258 cases with results from test Pap, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of the self-organizing map (SOM). An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps. The results of the SOM application were encouraging and produced maps depicting the important tests.
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