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Facial expression is an important component of the pain measurement process, which can be utilized by clinicians in patients with impaired communicative or cognitive abilities. However, given the complex nature of the pain and the absence of a standard reference, accurate measurement of pain in hospital settings is a difficult practice. In this paper, we present a vision-based measurement system aimed at automatically measuring the pain intensity values through the analysis of the facial expression, based on deep-learning strategies. For the scope of providing a metrological characterization of the system, we designed a benchmark for the estimation of the measurement precision via multiexpert judgments. As a major contribution of this paper, we investigated the interobserver and intraobserver variability as sources of uncertainty at the calibration level in order to provide a robust pain estimation via an automatic framework. Reproducibility and standard reference uncertainty (due to interobserver and intraobserver variability, respectively) have been estimated, modeled, and propagated through the proposed platform using the Guide to the expression of Uncertainty in Measurement (GUM)-compliant procedures. Numerical results, provided in terms of average values and confidence intervals of the accuracy of classification, indicate that such a system can facilitate decision-making in healthcare. Moreover, the proposed benchmark offers new hints to incorporate, rationally, subjective expert judgments within automatic measurement systems.

Vision-based measurement (VBM) systems are powerful tool to extract quantitative information by acquiring video sequence or static images. When a VBM is applied to the evaluation of nominal properties, such as image characteristics, the term VBM is substituted with vision-based evaluation (VBE) by extending the framework of operation unit to a new concept of evaluation unit (EU) for the image analysis and machine learning phases. To this regard, deep learning (DL) approaches have gained an exponential interest in the research and industrial community, thanks to incredible flexibility toward visual words and the possibility to abandon subjective feature extraction procedures. From such an explosiveness of applications, it emerges the need to conduct studies on the capability of DL strategies to deal with uncertainty contributions, i.e., definitional uncertainty related to the measurand and reference uncertainty that may occur during the calibration process. In order to present a benchmark platform to analyze the effect of the major sources of uncertainties estimated, we use here an atomic force microscopy (AFM) imaging scenario for the evaluation of the effect of nanoparticles exposure on human cells in the laboratory. These studies are nowadays fundamental in toxicity analysis for monitoring the health conditions of workers and for protecting people from atherosclerosis disease. The performance of the proposed VBE-DL system to recognize cell alterations from the AFM images is related to three different sources of uncertainty and a critical analysis of the results achieved is provided.

Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.