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The quality of the very early emotional experiences in a child's life are recognized today as foundational in the mental health of becoming an adult. Unsurprisingly, clinicians and health authorities agree on the importance of early detection and treatment of any mental health issues experienced by the preschool child. This is even more important and challenging as young children do not yet have the language skills necessary to express their distress. But do the caregivers in charge of these populations have the best tools at their disposal to carry out that important mission in an efficient and scientifically robust manner reflecting the latest research findings and scientific literature? This is the question we try to answer in this paper. We revisited the theoretical underpinnings behind the concept of mentalization as playing a key role in the ability to understand the other's inner reality. We then carried out an extensive review of scientific papers written over the past 30 years on the subject. We examined mental representations of the adult on the child and analyzed those mentalizations as key inputs. We focused on extracting cross analyses of the mentalizations of one child by several adults with different functions in the life of the child. In this process, we used the PRISMA methodology applied to large international medical databases. We found that the qualitative approach, usually associated with a lengthy process, has been overwhelmingly used in trying to assess patients' mental states and to analyze the mentalizations on the patient, adult or child. Furthermore, cross analyses of mentalizations from more than one adult on a given child were almost nonexistent, despite having high epistemic and clinical potential. There is a dearth of reliable quantitative tools, able to capture and analyze the mentalizations on a patient, that are scientifically robust, transposable, and easy to implement in a clinical setting. The absence of such tools for medical professionals in charge of assessing preschool children contributes, in our view, the low detection and poor treatment of mental disorders in that particularly vulnerable population. We believe there is here an urgent unmet need and that such tools should be designed, validated, and deployed into the target populations as early as possible. [ABSTRACT FROM AUTHOR]

The use of smartphones cameras for photogrammetric purposes is increasing. However, the suitability of smartphones for 3D modelling for medical purposes in general, and for cranial deformation in particular, is still to be analysed. This paper investigates the suitability of smartphone video cameras to create 3D models for cranial deformation analysis compared to the digital single-lens reflex (SLR) cameras traditionally used in close-range photogrammetry. Two models are obtained, the first one from a slow-motion video recorded with a smartphone, and the second one from SLR camera imagery. The models are compared to evaluate the differences not only between themselves but also through the best fitting ellipsoid that allow the determination of the cranial deformations. The average distance between models is 0.5 mm, and below 1 mm for 86% of the model points. The maximum difference between the two fitted ellipsoid semiaxes is 1 mm. It can be stated that smartphones are a low-cost solution that can provide 3D models with a similar accuracy to that of SLR cameras for non-static objects in close range scenarios. More interestingly, slow-motion videos provide comparable results in real clinical conditions with infants in movement. [ABSTRACT FROM AUTHOR]

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation. [Copyright &y& Elsevier]