Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years

Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1 . We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2 , selected using World Health Organization recommendations for growth standards3 . Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5 . The atlas was produced using 1,059 optimal quality, three dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6–8 . The atlas corresponds structurally to published magnetic resonance images9 , but with finer anatomical details in deep grey matter. The between study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks’ gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks’ gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment

Design of a series visco-elastic actuator for multi-purpose rehabilitation haptic device

<p>Abstract</p> <p>Background</p> <p>Variable structure parallel mechanisms, actuated with low-cost motors with serially added elasticity (series elastic actuator - SEA), has considerable potential in rehabilitation robotics. However, reflected masses of a SEA and variable structure parallel mechanism linked with a compliant actuator result in a potentially unstable coupled mechanical oscillator, which has not been addressed in previous studies.</p> <p>Methods</p> <p>The aim of this paper was to investigate through simulation, experimentation and theoretical analysis the necessary conditions that guarantee stability and passivity of a haptic device (based on a variable structure parallel mechanism driven by SEA actuators) when in contact with a human. We have analyzed an equivalent mechanical system where a dissipative element, a mechanical damper was placed in parallel to a spring in SEA.</p> <p>Results</p> <p>The theoretical analysis yielded necessary conditions relating the damping coefficient, spring stiffness, both reflected masses, controller's gain and desired virtual impedance that needs to be fulfilled in order to obtain stable and passive behavior of the device when in contact with a human. The validity of the derived passivity conditions were confirmed in simulations and experimentally.</p> <p>Conclusions</p> <p>These results show that by properly designing variable structure parallel mechanisms actuated with SEA, versatile and affordable rehabilitation robotic devices can be conceived, which may facilitate their wide spread use in clinical and home environments.</p

Theology and cosmology beyond the Big Bang theory

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Reduced costs with bisoprolol treatment for heart failure - An economic analysis of the second Cardiac Insufficiency Bisoprolol Study (CIBIS-II)

Background Beta-blockers, used as an adjunctive to diuretics, digoxin and angiotensin converting enzyme inhibitors, improve survival in chronic heart failure. We report a prospectively planned economic analysis of the cost of adjunctive beta-blocker therapy in the second Cardiac Insufficiency BIsoprolol Study (CIBIS II). Methods Resource utilization data (drug therapy, number of hospital admissions, length of hospital stay, ward type) were collected prospectively in all patients in CIBIS . These data were used to determine the additional direct costs incurred, and savings made, with bisoprolol therapy. As well as the cost of the drug, additional costs related to bisoprolol therapy were added to cover the supervision of treatment initiation and titration (four outpatient clinic/office visits). Per them (hospital bed day) costings were carried out for France, Germany and the U.K. Diagnosis related group costings were performed for France and the U.K. Our analyses took the perspective of a third party payer in France and Germany and the National Health Service in the U.K. Results Overall, fewer patients were hospitalized in the bisoprolol group, there were fewer hospital admissions perpatient hospitalized, fewer hospital admissions overall, fewer days spent in hospital and fewer days spent in the most expensive type of ward. As a consequence the cost of care in the bisoprolol group was 5-10% less in all three countries, in the per them analysis, even taking into account the cost of bisoprolol and the extra initiation/up-titration visits. The cost per patient treated in the placebo and bisoprolol groups was FF35 009 vs FF31 762 in France, DM11 563 vs DM10 784 in Germany and pound 4987 vs pound 4722 in the U.K. The diagnosis related group analysis gave similar results. Interpretation Not only did bisoprolol increase survival and reduce hospital admissions in CIBIS II, it also cut the cost of care in so doing. This `win-win' situation of positive health benefits associated with cost savings is Favourable from the point of view of both the patient and health care systems. These findings add further support for the use of beta-blockers in chronic heart failure

Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnostic accuracy, as it yields more information per scan than regular CT. However, the shear workload involved with analyzing a large number of scans drives the need for automated diagnosis methods. Therefore, we propose a detection and classification system for lung nodules in CT scans. Furthermore, we want to observe whether spectral images can increase classifier performance. For the detection of nodules we trained a VGG-like 3D convolutional neural net (CNN). To obtain a primary tumor classifier for our dataset we pre-trained a 3D CNN with similar architecture on nodule malignancies of a large publicly available dataset, the LIDC-IDRI dataset. Subsequently we used this pre-trained network as feature extractor for the nodules in our dataset. The resulting feature vectors were classified into two (benign/malignant) and three (benign/primary lung cancer/metastases) classes using support vector machine (SVM). This classification was performed both on nodule- and scan-level. We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. Classification performance on our own dataset was higher for scan- than for nodule-level predictions. For the three-class scan-level classification we obtained an accuracy of 78\%. Spectral features did increase classifier performance, but not significantly. Our work suggests that a pre-trained feature extractor can be used as primary tumor origin classifier for lung nodules, eliminating the need for elaborate fine-tuning of a new network and large datasets. Code is available at \url{https://github.com/tueimage/lung-nodule-msc-2018}

Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning

The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultrasound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation