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The images generated in modern IC laboratories are created with high-quality standard (1,024 × 1,024 pixels and 10-12 bits/pixel) enabling cardiologists to perform interventions in the best conditions. But these images are in most of the cases archived in a basic quality standard (512 × 512 pixels and 8 bits/pixel). The purpose of this work is to complete the research developed in a previous paper and analyze the influence of the matrix size and the bit depth reduction on the image quality acquired on a polymethylmethacrylate (PMMA) phantom with a test object. The variation in contrast-to-noise ratio (CNR) and high contrast spatial resolution (HCSR) were investigated when the matrix size and the bit depth were independently modified for different phantom thicknesses. These two image quality parameters did not suffer noticeable alterations under bits depth reduction from 10 to 8 bits. Such a result seems to imply that bits depth reduction could be used to reduce file sizes with a suitable algorithm and without losing perceptible image quality information. But when the matrix size was reduced from 1,024 × 1,024 to 512 × 512 pixels, a reduction from 17% to 25% in HCSR was noticed when changing phantom thickness, and an increase of 27% in CNR was observed. These findings should be taken into account and it would be wise to conduct further investigations in the field of clinical images. [ABSTRACT FROM AUTHOR]

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs. [ABSTRACT FROM AUTHOR]

The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as 'good balance'. Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. In this paper, we investigate whether it is best to optimize the balance or to settle for a less than optimal balance and use double robust estimation to adjust for remaining differences. We compare treatment effect estimates from regression, propensity score weighting, and double robust estimation with varying levels of effort expended to achieve balance using data from a study about the differences in outcomes by HIV status in heterosexually active homeless men residing in Los Angeles. Because of how costly data collection efforts are for this population, it is important to find an alternative estimation method that does not reduce effective sample size as much as methods that aggressively aim to optimize balance. Results from a simulation study suggest that there are instances in which we can obtain more precise treatment effect estimates without increasing bias too much by using a combination of regression and propensity score weights that achieve a less than optimal balance. There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance. [ABSTRACT FROM AUTHOR]

The article presents a study that describes the specialized compression algorithm EDLZW for efficient electrocardiograph data transmission. Topics include medical information system, wireless communication, compression ratio, system architecture of ECG monitoring system, medical data, and health data.

Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segment eye region of aged people. Hence, computer-aided diagnosis of anterior segment eye abnormalities will be helpful for mass screening and grading in ophthalmology. In this paper, we propose a multiclass computer-aided diagnosis (CAD) system using visible wavelength (VW) eye images to diagnose anterior segment eye abnormalities. In the proposed method, the input VW eye images are pre-processed for specular reflection removal and the iris circle region is segmented using a circular Hough Transform (CHT)-based approach. The first-order statistical features and wavelet-based features are extracted from the segmented iris circle and used for classification. The Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO) algorithm was used for the classification. In experiments, we used 228 VW eye images that belong to three different classes of anterior segment eye abnormalities. The proposed method achieved a predictive accuracy of 96.96% with 97% sensitivity and 99% specificity. The experimental results show that the proposed method has significant potential for use in clinical applications. [ABSTRACT FROM AUTHOR]

Purpose: Large inter-individual differences in warfarin maintenance dose are mostly due to the effect of genetic polymorphisms in multiple genes, including vitamin K epoxide reductase complex 1 (VKORC1), cytochromes P450 2C9 (CYP2C9), and cytochrome P450 4F2 (CYP4F2). Thus, several algorithms for predicting the warfarin dose based on pharmacogenomics data with clinical characteristics have been proposed. Although these algorithms consider these genetic polymorphisms, the formulas have different coefficient values that are critical in this context. In this study, we assessed the mutual validity among these algorithms by specifically considering racial differences. Methods: Clinical data including actual warfarin dose (AWD) of 125 Japanese patients from our previous study (Eur J Clin Pharmacol 65(11):1097–1103, 2009) were used as registered data that provided patient characteristics, including age, sex, height, weight, and concomitant medications, as well as the genotypes of CYP2C9 and VKORC1. Genotyping for CYP4F2*3 was performed by the PCR method. Five algorithms that included these factors were selected from peer-reviewed articles. The selection covered four populations, Japanese, Chinese, Caucasian, and African-American, and the International Warfarin Pharmacogenetics Consortium (IWPC). Results: For each algorithm, we calculated individual warfarin doses for 125 subjects and statistically evaluated its performance. The algorithm from the IWPC had the statistically highest correlation with the AWD. Importantly, the calculated warfarin dose (CWD) using the algorithm from African-Americans was less correlated with the AWD as compared to those using the other algorithms. The integration of CYP4F2 data into the algorithm did not improve the prediction accuracy. Conclusion: The racial difference is a critical factor for warfarin dose predictions based on pharmacogenomics. [ABSTRACT FROM AUTHOR]

To reduce phenotypic heterogeneity of Autism spectrum disorders (ASD) and add to the current diagnostic discussion this study aimed at identifying clinically meaningful ASD subgroups. Cluster analyses were used to describe empirically derived groups based on the Autism Diagnostic Interview-revised (ADI-R) in a large sample of n = 463 individuals with ASD aged 3-21. Three clusters were observed. Most severely affected individuals regarding all core symptoms were allocated to cluster 2. Cluster 3 comprised moderate symptom severity of social communication impairments (SCI) and less stereotyped repetitive behavior (RRB). Minor SCI and relatively more RRB characterized cluster 1. This study offers support for both, a symptom profile, and a gradient model of ASD within the spectrum due to the sample included. [ABSTRACT FROM AUTHOR]

This study examined the discriminative ability of the revised Autism Diagnostic Observation Schedule module 4 algorithm (Hus and Lord in J Autism Dev Disord 44(8):1996-2012, ) in 93 Dutch males with Autism Spectrum Disorder (ASD), schizophrenia, psychopathy or controls. Discriminative ability of the revised algorithm ASD cut-off resembled the original algorithm ASD cut-off: highly specific for psychopathy and controls, lower sensitivity than Hus and Lord (; i.e. ASD .61, AD .53). The revised algorithm AD cut-off improved sensitivity over the original algorithm. Discriminating ASD from schizophrenia was still challenging, but the better-balanced sensitivity (.53) and specificity (.78) of the revised algorithm AD cut-off may aide clinicians' differential diagnosis. Findings support using the revised algorithm, being conceptually conform the other modules, thus improving comparability across the lifespan. [ABSTRACT FROM AUTHOR]

Due to the predominance of boys diagnosed with autism spectrum disorders (ASD), girls are rarely studied independently. Research specifically focusing on play and social-communication in girls with ASD is extremely varied. We were interested in whether girls with ASD demonstrated equivalent social-communication and play skills in early childhood relative to boys, using two measures focused on the specific quantification of these variables. We also examined whether the associations between developmental variables and social-communication and play differed by gender. Forty girls with ASD were individually matched to 40 boys based on ASD severity. Our results suggest that girls and boys were more similar than different, however they also raise questions about the potential differential associations between development and requesting ability in girls and boys with ASD. [ABSTRACT FROM AUTHOR]

We studied autistics by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma and beta bands, lateralized to the right hemisphere. When comparing VwA versus the V-A in the midline regions, we found significant decrements of absolute PSD for delta, theta and alpha, and increments for the beta and gamma bands. In autistics, VwA versus V-A tended to show lower coherence values in the right hemisphere. An impairment of visual and auditory sensory integration in autistics might explain our results. [ABSTRACT FROM AUTHOR]

This study focused on strengths and variables that contribute to marital successes for couples with children with autism spectrum disorders (in this article, referred to as 'autism'). Particularly, the purpose of this study was to examine what husbands and wives with children with autism in contrast to couples with children who are typically developing identify as helpful to maintaining their marriages. Concept mapping methodology was used for this research study. Couples with children with autism and couples with children who are typically developing participated in telephone interviews and then grouped and rated the statements generated from their interviews. Groupings were translated into pictorial maps showing relationships and patterns. Couples with children with autism shared common perceptions about factors that help to keep their marriages strong: communication and shared foundational ideas about marriage. Communication was a cluster for all groups of husbands and wives. Only mothers of children with autism identified time for self-care as a distinct cluster. [ABSTRACT FROM AUTHOR]

Study design:Cross-section design.Objectives:The development of reliable screen technology for predicting those at risk of depression in the long-term remains a challenge. The objective of this research was to determine factors that classify correctly adults with spinal cord injury (SCI) with depressed mood and to develop a diagnostic algorithm that could be applied for prediction of depressed mood in the long-term.Setting:SCI rehabilitation unit, rehabilitation outpatient clinic and Australian community.Methods:Participants included 107 adults with SCI. The assessment regimen included demographic and injury variables, negative mood states, pain intensity, health-related quality of life and self-efficacy. Participants were divided into those with 'normal' mood versus those with elevated depressed mood. Discriminant function analysis (DFA) was then used to isolate factors that in combination, best classify the presence or absence of depressed mood.Results:At the time of assessment, 24 participants (22.4%) had elevated depressed mood. DFA identified six factors that discriminated between those with depressed mood (P<0.01) and those with normal mood, explaining 61% of the variance. Factors consisted of pain intensity, mental health, emotional and social functioning, self-efficacy and fatigue. DFA correctly classified 91.7% (n=22 of 24) of those with depressed mood and 95.2% (n=79 of 83) of those without. Demographic, injury and physical health function variables were not found to discriminate depressed mood.Conclusion:Clinical implications of applying a diagnostic algorithm for detecting depression in adults with SCI are discussed. Prospective research is needed to test the predictive efficacy of the algorithm. [ABSTRACT FROM AUTHOR]

Although it is still unclear what causes autism spectrum disorders (ASDs), over time researchers and clinicians have become more precise with detecting and diagnosing ASD. Many diagnoses, however, are based on the criteria established within the Diagnostic and Statistical Manual of Mental Disorders ( DSM); thus, any change in these diagnostic criteria can have a great effect upon children with ASD and their families. It is predicted that the prevalence of ASD diagnoses will dramatically decrease with the adoption of the proposed DSM- 5 criteria in 2013. The aim of this current study was to inspect the changes in prevalence first using a diagnostic criteria set which was modified slightly from the DSM- 5 criteria (Modified-1 criteria) and again using a set of criteria which was relaxed even a bit more (Modified-2 criteria). Modified-1 resulted in 33.77 % fewer toddlers being diagnosed with ASD compared to the DSM- IV, while Modified-2 resulted in only a 17.98 % decrease in ASD diagnoses. Children diagnosed with the DSM- 5 criteria exhibited the greatest levels of autism symptomatology, but the Mod-1, Mod-2, and DSM- IV groups still demonstrated significant impairments. Implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

This prospective observational study evaluates the validity of an algorithm for assigning patients to a multidisciplinary modularized managed care headache treatment program. N = 545 chronic headache sufferers [migraine (53.8 %), migraine + tension type (30.1 %), tension type (8.3 %) or medication overuse headache (6.2 %), other primary headaches (1.5 %)] were assigned to one of four treatment modules differing with regard to the number and types of interventions entailed (e.g., medication, psychological intervention, physical therapy, etc.). A rather simple assignment algorithm based on headache frequency, medication use and psychiatric comorbidity was used. Patients in the different modules were compared with regard to the experienced burden of disease. 1-year follow-up outcome data are reported ( N = 160). Headache frequency and analgesic consumption differed significantly among patients in the modules. Headache-related disability was highest in patients with high headache frequency with/without medication overuse or psychiatric comorbidity (modules 2/3) compared to patients with low headache frequency and medication (module 0). Physical functioning was lowest in patients with chronic headache regardless of additional problems (modules 1/2/3). Psychological functioning was lowest in patients with severe chronicity with/without additional problems (module 2/3) compared to headache suffers with no/moderate chronicity (module 0/1). Anxiety or depression was highest in patients with severe chronicity. In 1-year follow-up, headache frequency (minus 45.3 %), consumption of attack-aborting drugs (minus 71.4 %) and headache-related disability decreased (minus 35.9 %). Our results demonstrate the clinical effectiveness and the criterion validity of the treatment assignment algorithm based on headache frequency, medication use and psychiatric comorbidity. [ABSTRACT FROM AUTHOR]

The article presents a methodology for the development of a Bayesian classifier for the diagnosis of common dental diseases using a set of 14 pain parameters. Also cited are the naïve Bayesian classifier (NBC), the Learned Bayes' classifier (LBC), as well as some dental diseases like acute pulpitis, Apical periodontitis, and chronic pulpitis.

The article discusses the feasible use of the Artificial Immune Recognition System (AIRS) proposed by Andrew Watkins in assisting doctors with the prediction of the probability of type 2 diabetes in pregnant women. Topics discussed include the statistics from the World Health Organization (WHO) as well as the American Diabetes Association (ADA) on the number of people afflicted with diabetes as in diabetes mellitus, the support vector machines (SVM), and the vertebrae immune system.