Nowadays, visual-based measurement (VBM) systems offer new possibilities of investigation for researchers and clinicians, using noninvasive-nondestructive approaches. In this paper, we present an atomic force microscopy (AFM)-based VBM for the study of cell-cerium oxide nanoparticle interactions. To provide a metrological characterization of the results obtained and with the aim to compare different strategies, we modeled four artifacts effects occurring in AFM acquisition within the random process theory and implemented a Monte Carlo simulation to repeatedly inject such variability in the original image. Empirical cumulative distribution function, confidence intervals, and average representative values, following Supplement 1 guidelines, were estimated for the final scores assigned by the operations unit to each cell. Area under the roc curve and accuracy of classification for two different machine learning approaches were compared in a metrological compliant methodology. Results clearly demonstrate the robustness of the presented VBM system and quantify the uncertainty expected for such kind of results.
Mencattini, A., Casti, P., Fazio, G., Ghibelli, L., Luce, M., Cricenti, A., et al. (2018). Uncertainty Evaluation of a VBM System for AFM Study of Cell-Cerium Oxide Nanoparticles Interactions. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 67(7), 1564-1572 [10.1109/TIM.2018.2799118].
Uncertainty Evaluation of a VBM System for AFM Study of Cell-Cerium Oxide Nanoparticles Interactions
Mencattini A.
;CASTI, PAOLA;Fazio G.;Ghibelli L.;Martinelli E.;Di Natale C.
2018-01-01
Abstract
Nowadays, visual-based measurement (VBM) systems offer new possibilities of investigation for researchers and clinicians, using noninvasive-nondestructive approaches. In this paper, we present an atomic force microscopy (AFM)-based VBM for the study of cell-cerium oxide nanoparticle interactions. To provide a metrological characterization of the results obtained and with the aim to compare different strategies, we modeled four artifacts effects occurring in AFM acquisition within the random process theory and implemented a Monte Carlo simulation to repeatedly inject such variability in the original image. Empirical cumulative distribution function, confidence intervals, and average representative values, following Supplement 1 guidelines, were estimated for the final scores assigned by the operations unit to each cell. Area under the roc curve and accuracy of classification for two different machine learning approaches were compared in a metrological compliant methodology. Results clearly demonstrate the robustness of the presented VBM system and quantify the uncertainty expected for such kind of results.File | Dimensione | Formato | |
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