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Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS-Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.

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Measurement uncertainty is one of the widespread concepts applied in scientific works, particularly to estimate the accuracy of measurement results and to evaluate the conformity of products and processes. In this work, we propose a methodology to analyze the performance of measurement systems existing in the design phases, based on a probabilistic approach, by applying the Monte Carlo method (MCM). With this approach, it is feasible to identify the dominant contributing factors of imprecision in the evaluated system. In the design phase, this information can be used to identify where the most effective attention is required to improve the performance of equipment. This methodology was applied over a simulated electrocardiogram (ECG), for which a measurement uncertainty of the order of 3.54% of the measured value was estimated, with a confidence level of 95%. For this simulation, the ECG computational model was categorized into two modules: the preamplifier and the final stage. The outcomes of the analysis show that the preamplifier module had a greater influence on the measurement results over the final stage module, which indicates that interventions in the first module would promote more significant performance improvements in the system. Finally, it was identified that the main source of ECG measurement uncertainty is related to the measurand, focused towards the objective of better characterization of the metrological behavior of the measurements in the ECG.

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Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected persons, divided into benign, malignant and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the bilateral algorithm together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection.

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OBJECTIVES: To investigate independent relationships of childhood linear growth (height gain) and relative weight gain to adult cardiovascular disease (CVD) risk traits in Asian Indians. STUDY DESIGN: Data from 2218 adults from the Vellore Birth Cohort were examined for associations of cross-sectional height and body mass index (BMI) and longitudinal growth (independent conditional measures of height and weight gain) in infancy, childhood, adolescence, and adulthood with adult waist circumference (WC), blood pressure (BP), insulin resistance (homeostatic model assessment-insulin resistance [HOMA-IR]), and plasma glucose and lipid concentrations. RESULTS: Higher BMI/greater conditional relative weight gain at all ages was associated with higher adult WC, after 3 months with higher adult BP, HOMA-IR, and lipids, and after 15 years with higher glucose concentrations. Taller adult height was associated with higher WC (men ß = 2.32 cm per SD, women ß = 1.63, both P < .001), BP (men ß = 2.10 mm Hg per SD, women ß = 1.21, both P ≤ .001), and HOMA-IR (men ß = 0.08 log units per SD, women ß = 0.12, both P ≤ .05) but lower glucose concentrations (women ß = -0.03 log mmol/L per SD P = .003). Greater height or height gain at all earlier ages were associated with higher adult CVD risk traits. These positive associations were attenuated when adjusted for adult BMI and height. Shorter length and lower BMI at birth were associated with higher glucose concentration in women. CONCLUSIONS: Greater height or weight gain relative to height during childhood or adolescence was associated with a more adverse adult CVD risk marker profile, and this was mostly attributable to larger adult size.

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OBJECTIVE: To investigate whether abnormal regional white matter architecture in the perisylvian region could be used as an easy and sensitive quantitative method to demonstrate language pathway abnormalities in children with developmental delay (DD). STUDY DESIGN: We performed diffusion tensor imaging in 15 DD subjects (age, 61.1 ± 20.9 months) and 15 age-matched typically developing (TD) children (age, 68.4 ± 19.2 months). With diffusion tensor imaging color-coded orientation maps, we quantified the fraction of fibers in the perisylvian region that are oriented in anteroposterior (AP) and mediolateral (ML) directions, and their ratio (AP/ML) was calculated. RESULTS: The AP/ML ratio was more sensitive than tractography in characterizing perisylvian regional abnormalities in DD children. The AP/ML ratio of the left perisylvian region was significantly lower in DD children compared with TD children (P = .03). The ML component of bilateral perisylvian regions was significantly higher in DD children compared with TD children (P = .01 [left] and P = .004 [right]). No significant difference was found in the AP component in the two groups. A significant negative correlation of the left ML component with Vineland communication skills was observed (r = -0.657, P = .011). CONCLUSIONS: The AP/ML ratio appears to be a sensitive indicator of regional white matter architectural abnormalities in the perisylvian region of DD children.

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OBJECTIVE: To investigate cortical association tracts using diffusion tensor imaging (DTI) in children with global developmental delay of unknown etiology. STUDY DESIGN: We performed DTI in 20 patients (age range: 18-83 months, mean: 45 +/- 16 months, 12 males) with a history of global developmental delay and 10 typically developing children (age range: 26-99 months, mean: 54 +/- 24 months, 5 males). DTI tractography was performed to isolate major cortical association tracts. RESULTS: In 9 out of 20 patients, arcuate fasciculus (AF) was absent bilaterally and in another 2 patients, it was absent in left hemisphere. In contrast, AF was present bilaterally in all typically developing children. Fractional Anisotropy (FA) of inferior longitudinal fasciculus (ILF) was asymmetric in the control group but not in the developmental delay group (P = .04). FA was significantly reduced in right ILF in developmentally delayed children compared with controls (P = .03). FA of other association tracts was not different between patients and controls (P = NS). The apparent diffusion coefficient (ADC) showed no asymmetry for these tracts in controls or developmentally delayed children (P = NS). CONCLUSIONS: DTI can be used to identify absence of AF and inadequate maturation of ILF in children with global developmental delay of unknown etiology.

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