Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images

As a non-hazardous and non-invasive approach to medical diagnostic imaging, ultrasound serves as an ideal candidate for tracking and monitoring pregnancy development. One critical assessment during the first trimester of the pregnancy is the size measurements of the Gestation Sac (GS) and the Yolk Sac (YS) from ultrasound images. Such measurements tend to give a strong indication on the viability of the pregnancy. This paper proposes a novel multi-level trainable segmentation method to achieve three objectives in the following order: (1) segmenting and measuring the GS, (2) automatically identifying the stage of pregnancy, and (3) segmenting and measuring the YS. The first level segmentation employs a trainable segmentation technique based on the histogram of oriented gradients to segment the GS and estimate its size. This is then followed by an automatic identification of the pregnancy stage based on histogram analysis of the content of the segmented GS. The second level segmentation is used after that to detect the YS and extract its relevant size measurements. A trained neural network classifier is employed to perform the segmentation at both levels. The effectiveness of the proposed solution has been evaluated by comparing the automatic size measurements of the GS and YS against the ones obtained gynaecologist. Experimental results on 199 ultrasound images demonstrate the effectiveness of the proposal in producing accurate measurements as well as identifying the correct stage of pregnancy

A nearly continuous measure of birth weight for gestational age using a United States national reference

BACKGROUND: Fully understanding the determinants and sequelae of fetal growth requires a continuous measure of birth weight adjusted for gestational age. Published United States reference data, however, provide estimates only of the median and lowest and highest 5(th )and 10(th )percentiles for birth weight at each gestational age. The purpose of our analysis was to create more continuous reference measures of birth weight for gestational age for use in epidemiologic analyses. METHODS: We used data from the most recent nationwide United States Natality datasets to generate multiple reference percentiles of birth weight at each completed week of gestation from 22 through 44 weeks. Gestational age was determined from last menstrual period. We analyzed data from 6,690,717 singleton infants with recorded birth weight and sex born to United States resident mothers in 1999 and 2000. RESULTS: Birth weight rose with greater gestational age, with increasing slopes during the third trimester and a leveling off beyond 40 weeks. Boys had higher birth weights than girls, later born children higher weights than firstborns, and infants born to non-Hispanic white mothers higher birth weights than those born to non-Hispanic black mothers. These results correspond well with previously published estimates reporting limited percentiles. CONCLUSIONS: Our method provides comprehensive reference values of birth weight at 22 through 44 completed weeks of gestation, derived from broadly based nationwide data. Other approaches require assumptions of normality or of a functional relationship between gestational age and birth weight, which may not be appropriate. These data should prove useful for researchers investigating the predictors and outcomes of altered fetal growth

The performance of the World Rugby Head Injury Assessment Screening Tool: a diagnostic accuracy study

Abstract Background Off-field screening tools, such as the Sports Concussion Assessment Tool (SCAT), have been recommended to identify possible concussion following a head impact where the consequences are unclear. However, real-life performance, and diagnostic accuracy of constituent sub-tests, have not been well characterized. Methods A retrospective cohort study was performed in elite Rugby Union competitions between September 2015 and June 2018. The study population comprised consecutive players identified with a head impact event undergoing off-field assessments with the World Rugby Head Injury Assessment (HIA01) screening tool, an abridged version of the SCAT3. Off-field screening performance was investigated by evaluating real-life removal-from-play outcomes and determining the theoretical diagnostic accuracy of the HIA01 tool, and individual sub-tests, if player-specific baseline or normative sub-test thresholds were strictly applied. The reference standard was clinically diagnosed concussion determined by serial medical assessments. Results One thousand one hundred eighteen head impacts events requiring off-field assessments were identified, resulting in 448 concussions. Real-life removal-from-play decisions demonstrated a sensitivity of 76.8% (95% CI 72.6–80.6) and a specificity of 86.6% (95% CI 83.7–89.1) for concussion (AUROC 0.82, 95% CI 0.79–0.84). Theoretical HIA01 tool performance worsened if pre-season baseline values (sensitivity 89.6%, specificity 33.9%, AUROC 0.62, p < 0.01) or normative thresholds (sensitivity 80.4%, specificity 69.0%, AUROC 0.75, p < 0.01) were strictly applied. Symptoms and clinical signs were the HIA01 screening tool sub-tests most predictive for concussion; with immediate memory and tandem gait providing little additional diagnostic value. Conclusions These findings support expert recommendations that clinical judgement should be used in the assessment of athletes following head impact events. Substitution of the tandem gait and 5-word immediate memory sub-tests with alternative modes could potentially improve screening tool performance