Skin measurement devices to assess skin quality:A systematic review on reliability and validity

Background Many treatments aim to slow down or reverse the visible signs of skin aging and thereby improve skin quality. Measurement devices are frequently employed to measure the effects of these treatments to improve skin quality, for example, skin elasticity, color, and texture. However, it remains unknown which of these devices is most reliable and valid. Materials and methods MEDLINE, Embase, Cochrane Central, Web of Science, and Google Scholar databases were searched. Instruments were scored on reporting construct validity by means of convergent validity, interobserver, intraobserver, and interinstrument reliability. Results For the evaluation of skin color, 11 studies were included describing 16 measurement devices, analyzing 3172 subjects. The most reliable device for skin color assessment is the Minolta Chromameter CR-300 due to good interobserver, intraobserver, and interinstrument reliability. For skin elasticity, seven studies assessed nine types of devices analyzing 290 subjects in total. No intra and interobserver reliability was reported. Skin texture was assessed in two studies evaluating 72 subjects using three different types of measurement devices. The PRIMOS device reported excellent intra and interobserver reliability. None of the included reviewed devices could be determined to be valid based on construct validity. Conclusion The most reliable devices to evaluate skin color and texture in ordinary skin were, respectively, the Minolta Chromameter and PRIMOS. No reliable device is available to measure skin elasticity in ordinary skin and none of the included devices could be determined to be designated as valid

Growth patterns and shape development of the paediatric mandible – A 3D statistical model

BACKGROUND/AIM: To develop a 3D morphable model of the normal paediatric mandible to analyse shape development and growth patterns for males and females. METHODS: Computed tomography (CT) data was collected for 242 healthy children referred for CT scan between 2011 and 2018 aged between 0 and 47 months (mean, 20.6 ± 13.4 months, 59.9% male). Thresholding techniques were used to segment the mandible from the CT scans. All mandible meshes were annotated using a defined set of 52 landmarks and processed such that all meshes followed a consistent triangulation. Following this, the mandible meshes were rigidly aligned to remove translation and rotation effects, while size effects were retained. Principal component analysis (PCA) was applied to the processed meshes to construct a generative 3D morphable model. Partial least squares (PLS) regression was also applied to the processed data to extract the shape modes with which to evaluate shape differences for age and sex. Growth curves were constructed for anthropometric measurements. RESULTS: A 3D morphable model of the paediatric mandible was constructed and validated with good generalisation, compactness, and specificity. Growth curves of the assessed anthropometric measurements were plotted without significant differences between male and female subjects. The first principal component was dominated by size effects and is highly correlated with age at time of scan (Spearman's r = 0.94, p < 0.01). As with PCA, the first extracted PLS mode captures much of the size variation within the dataset and is highly correlated with age (Spearman's r = −0.94, p < 0.01). Little correlation was observed between extracted shape modes and sex with either PCA or PLS for this study population. CONCLUSION: The presented 3D morphable model of the paediatric mandible enables an understanding of mandibular shape development and variation by age and sex. It allowed for the construction of growth curves, which contains valuable information that can be used to enhance our understanding of various disorders that affect the mandibular development. Knowledge of shape changes in the growing mandible has potential to improve diagnostic accuracy for craniofacial conditions that impact the mandibular morphology, objective evaluation, surgical planning, and patient follow-up

Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to simulate the outcome of a range of craniofacial surgical procedures. This opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes

Statistical modelling for hard and soft tissue of the human head

First proposed over 20 years ago, 3D morphable models (3DMMs) provide an insight into the statistical shape variations of a given shape class. Craniofacial anomalies represent a diverse class of conditions in which one or more of the sutures in the infant cranium fuses prematurely, causing a deviation in the growth pattern of the skull from that of the unaffected population. 3DMMs present an opportunity to analyse these shape deviations from a statistical perspective and can be used for the development of diagnostic and surgical planning tools. For this to be achieved, however, several boundary limitations must be overcome. Constructing statistical shape models for a population affected by craniosynostosis is by no means a straightforward task. As is suggested by the description “craniofacial anomalies”, instances of craniosynostosis are rare. Consequently, it can be difficult to collect sufficient quantities of data to accurately model the shape variations of the affected population. While 3D facial and cranial datasets are becoming increasingly available, these datasets tend to be comprised of adolescent and adult samples and few contain instances of infants and young children. Even when adequate 3D data has been collected, annotation is expensive and time consuming. Finally, while mesh autoencoders have proven effective for accurate mesh reconstruction and novel shape generation, there are many advances yet to be made in this domain. This thesis aims to make progress addressing the aforementioned issues in a number of ways. To address some of the existing limitations surrounding sparse 3D annotation, we present two methods for landmark localisation in 3D point clouds: i) point cloud segmentation and offset vector prediction are combined to localise landmarks within the input cloud and ii) the concept of convolutional pose machines is extended to predict landmark heatmaps for 3D facial point clouds. Both methods are shown to be robust to a range of input point cloud sizes and can be readily applied for landmark localisation in any shape class given appropriate training data. To aid in surgical reconstruction procedures for ear microtia we propose an approach to infer the underlying ear cartilage shape from that of the external ear soft tissue. We achieve this by generalising the convolutional mesh autoencoder framework such that the shape class and mesh topology of the encoder and decoder need not be identical. We further introduce an intersection loss to enforce the spatial relationship between the outer ear and underlying cartilage and improve the robustness of predicted results. As normal models of the paediatric population are limited, we collect a dataset of CT scans for infants aged between 0 and 48 months. From this dataset we construct 3DMMs of the face, head, skull, and mandible. By collating data from multiple sources, we build facial and head models to aid in the diagnosis of craniofacial anomalies related to mutations in the FGFR gene. High diagnostic accuracy is achieved, and experiments demonstrate that the facial features contain important information for the correct diagnosis of syndromic craniosynostosis. Finally, neural approaches to the construction of 3DMMs can result in a loss of interpretability and regularisation in the model latent space. To address this, we propose PCA retargeting, a method for expressing linear PCA models as convolutional mesh autoencoders and thereby retaining the gaussianity of the latent space.Open Acces

Skin measurement devices to assess skin quality: A systematic review on reliability and validity

Background: Many treatments aim to slow down or reverse the visible signs of skin aging and thereby improve skin quality. Measurement devices are frequently employed to measure the effects of these treatments to improve skin quality, for example, skin elasticity, color, and texture. However, it remains unknown which of these devices is most reliable and valid. Materials and methods: MEDLINE, Embase, Cochrane Central, Web of Science, and Google Scholar databases were searched. Instruments were scored on reporting construct validity by means of convergent validity, interobserver, intraobserver, and interinstrument reliability. Results: For the evaluation of skin color, 11 studies were included describing 16 measurement devices, analyzing 3172 subjects. The most reliable device for skin color assessment is the Minolta Chromameter CR-300 due to good interobserver, intraobserver, and interinstrument reliability. For skin elasticity, seven studies assessed nine types of devices analyzing 290 subjects in total. No intra and interobserver reliability was reported. Skin texture was assessed in two studies evaluating 72 subjects using three different types of measurement devices. The PRIMOS device reported excellent intra and interobserver reliability. None of the included reviewed devices could be determined to be valid based on construct validity. Conclusion: The most reliable devices to evaluate skin color and texture in ordinary skin were, respectively, the Minolta Chromameter and PRIMOS. No reliable device is available to measure skin elasticity in ordinary skin and none of the included devices could be determined to be designated as valid