Deep Learning for Identification of Acute Illness and Facial Cues of Illness

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3–33.1% for the skin model) to 89.4% (66.9–98.7%, for the nose model). Specificity ranged from 42.1% (20.3–66.5%) for the nose model and 94.7% (73.9–99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62–0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35–100.00%) and specificity of 42.11% (20.25–66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness

The effect of sleep deprivation on objective and subjective measures of facial appearance

This study was funded by the Swedish Research Council, FORTE (Swedish Research Council for Health, Working Life and Welfare), and The Swedish Foundation for Humanities and Social Sciences.The faces of people who are sleep deprived are perceived by others as looking paler, less healthy and less attractive compared to when well rested. However, there is little research using objective measures to investigate sleep‐loss‐related changes in facial appearance. We aimed to assess the effects of sleep deprivation on skin colour, eye openness, mouth curvature and periorbital darkness using objective measures, as well as to replicate previous findings for subjective ratings. We also investigated the extent to which these facial features predicted ratings of fatigue by others and could be used to classify the sleep condition of the person. Subjects (n = 181) were randomised to one night of total sleep deprivation or a night of normal sleep (8–9 hr in bed). The following day facial photographs were taken and, in a subset (n = 141), skin colour was measured using spectrophotometry. A separate set of participants (n = 63) later rated the photographs in terms of health, paleness and fatigue. The photographs were also digitally analysed with respect to eye openness, mouth curvature and periorbital darkness. The results showed that neither sleep deprivation nor the subjects’ sleepiness was related to differences in any facial variable. Similarly, there was no difference in subjective ratings between the groups. Decreased skin yellowness, less eye openness, downward mouth curvature and periorbital darkness all predicted increased fatigue ratings by others. However, the combination of appearance variables could not be accurately used to classify sleep condition. These findings have implications for both face‐to‐face and computerised visual assessment of sleep loss and fatigue.PostprintPeer reviewe

Structure and tissue distribution of some retinoid-binding proteins

Vitamin A has, apart from its function in the visual pigments, general effects on several organs. Early signs of vitamin A deficiency include keratinization of epithelia and hyperkeratosis of the skin. To elucidate a generalized function for vitamin A, we have taken the approach of tracing the vitamin from its storage site in the liver via its blood transport by the retinol-binding protein (RBP) to its uptake by susceptible cells. We have also examined the intracellular occurrence of vitamin A as regards its binding to specific receptor proteins. Here we summarize data on the amino acid sequences of several vitamin A-binding proteins. The finding that CRBP and CRABP, the two intracellular proteins, are homologous to each other, to a myelin protein, and to a fatty acid-binding protein may shed light on the functions of these proteins. Retinoic acid, which binds to CRABP but not CRBP, induces differentiation of teratocarcinoma cells. This is accompanied by a lowering of the CRABP concentration, an increase of the CRBP level, and an increase in the uptake of retinol from RBP. The epidermis contains both CRBP and CRABP, and their distributions are rather similar. However, in contrast to CRBP, CRABP is most abundant in cells lining the hair follicles. CRBP occurs in greatest relative amounts in the outer layers of the epidermis. Since techniques have been developed to measure CRBP and CRABP, normal and disease-affected skin may now be explored as to quantity and cellular distribution of the retinoid-binding proteins

Induced pseudoscalar coupling of the proton weak interaction

The induced pseudoscalar coupling $g_p$ is the least well known of the weak coupling constants of the proton's charged--current interaction. Its size is dictated by chiral symmetry arguments, and its measurement represents an important test of quantum chromodynamics at low energies. During the past decade a large body of new data relevant to the coupling $g_p$ has been accumulated. This data includes measurements of radiative and non radiative muon capture on targets ranging from hydrogen and few--nucleon systems to complex nuclei. Herein the authors review the theoretical underpinnings of $g_p$, the experimental studies of $g_p$, and the procedures and uncertainties in extracting the coupling from data. Current puzzles are highlighted and future opportunities are discussed.Comment: 58 pages, Latex, Revtex4, prepared for Reviews of Modern Physic

Maternal educational level, parental preventive behavior, risk behavior, social support and medical care consumption in 8-month-old children in Malmö, Sweden

<p>Abstract</p> <p>Background</p> <p>The social environment in which children grow up is closely associated with their health. The aim of this study was to investigate the relationship between maternal educational level, parental preventive behavior, parental risk behavior, social support, and use of medical care in small children in Malmö, Sweden. We also wanted to investigate whether potential differences in child medical care consumption could be explained by differences in parental behavior and social support.</p> <p>Methods</p> <p>This study was population-based and cross-sectional. The study population was 8 month-old children in Malmö, visiting the Child Health Care centers during 2003-2007 for their 8-months check-up, and whose parents answered a self-administered questionnaire (n = 9,289 children).</p> <p>Results</p> <p>Exclusive breast feeding ≥4 months was more common among mothers with higher educational level. Smoking during pregnancy was five times more common among less-educated mothers. Presence of secondhand tobacco smoke during the first four weeks of life was also much more common among children with less-educated mothers. Less-educated mothers more often experienced low emotional support and low practical support than mothers with higher levels of education (>12 years of education). Increased exposure to unfavorable parental behavioral factors (maternal smoking during pregnancy, secondhand tobacco smoke and exclusive breastfeeding <4 months) was associated with increased odds of in-hospital care and having sought care from a doctor during the last 8 months. The odds were doubled when exposed to all three risk factors. Furthermore, children of less-educated mothers had increased odds of in-hospital care (OR = 1.34 (95% CI: 1.08, 1.66)) and having sought care from a doctor during the last 8 months (OR = 1.28 (95% CI: 1.09, 1.50)), which were reduced and turned statistically non-significant after adjustment for unfavorable parental behavioral factors.</p> <p>Conclusion</p> <p>Children of less-educated mothers were exposed to more health risks, fewer health-promoting factors, worse social support, and had higher medical care consumption than children with higher educated mothers. After adjustment for parental behavioral factors the excess odds of doctor's visits and in-hospital care among children with less-educated mothers were reduced. Improving children's health calls for policies targeting parents' health-related behaviors and social support.</p