Caroline L. Knight

The effectiveness of DRL in the management and treatment of severe behaviour disorders following brain injury.

Effective management of behaviour disorders following brain injury is essential if individuals are to achieve their rehabilitation potential. Best practice dictates that the intrusiveness of any operant approach used be minimal, remain in operation for the shortest time possible, and emphasize skill building. Ideally, treatment gains should maintain following its withdrawal. Reinforcement methods fulfil these criteria in that they are less intrusive, concerned with the establishment of pro-social behaviours, and encourage positive staff-patient interaction. While their efficacy has been well documented with other clinical populations, less is known regarding treatment of behaviour disorders in survivors of brain injury. Some existing studies are characterized by methodological weakness that limit understanding of any contribution made to observed improvement, and little is known regarding maintenance of treatment effects. In this paper the effectiveness of a variant of differential reinforcement, DRL, will be examined. Three cases will be presented which demonstrate increased behavioural control in response to the use of DRL. A strength of this paper is that the use of appropriate single-case design methodology, and follow-up data up to 18 months after treatment, permits more robust conclusions regarding the efficacy of DRL to be made. These are discussed, together with practical points regarding programme design.

Management of systemic lupus erythematosus during pregnancy: challenges and solutions

Systemic lupus erythematosus (SLE) is a chronic, multisystem autoimmune disease predominantly affecting women, particularly those of childbearing age. SLE provides challenges in the prepregnancy, antenatal, intrapartum, and postpartum periods for these women, and for the medical, obstetric, and midwifery teams who provide their care. As with many medical conditions in pregnancy, the best maternal and fetal–neonatal outcomes are obtained with a planned pregnancy and a cohesive multidisciplinary approach. Effective prepregnancy risk assessment and counseling includes exploration of factors for poor pregnancy outcome, discussion of risks, and appropriate planning for pregnancy, with consideration of discussion of relative contraindications to pregnancy. In pregnancy, early referral for hospital-coordinated care, involvement of obstetricians and rheumatologists (and other specialists as required), an individual management plan, regular reviews, and early recognition of flares and complications are all important. Women are at risk of lupus flares, worsening renal impairment, onset of or worsening hypertension, preeclampsia, and/or venous thromboembolism, and miscarriage, intrauterine growth restriction, preterm delivery, and/or neonatal lupus syndrome (congenital heart block or neonatal lupus erythematosus). A cesarean section may be required in certain obstetric contexts (such as urgent preterm delivery for maternal and/or fetal well-being), but vaginal birth should be the aim for the majority of women. Postnatally, an ongoing individual management plan remains important, with neonatal management where necessary and rheumatology followup. This article explores the challenges at each stage of pregnancy, discusses the effect of SLE on pregnancy and vice versa, and reviews antirheumatic medications with the latest guidance about their use and safety in pregnancy. Such information is required to effectively and safely manage each stage of pregnancy in women with SLE.

Use of the Multiple Errands Test - Simplified Version in the assessment of suboptimal effort

Most measures of suboptimal effort focus on short-term learning; fewer studies have considered non-memory feigned cognitive impairment. This study investigated the utility of the Multiple Errands Test – Simplified Version (MET-SV) in the detection of feigned executive functioning impairment. Performance of simulating malingerers (Nu2009=u200947) was compared to acquired brain injury (Nu2009=u200946) and neurologically healthy control groups (Nu2009=u200950). Although simulating malingerers were successful at feigning a realistic level of impairment compared to the brain injury group, there were significant differences regarding pattern of performance. A logistic regression model successfully classified 84% of simulating malingerers and 74.5% of brain injured individuals. Receiver Operating Characteristic (ROC) analysis supported the discriminatory power of the model. The current study is unique in yielding some understanding of the real-life observation of suspected malingerers compared to individuals with genuine cognitive difficulties. Results suggest the MET-SV can contribute to the clinical assessment of individuals suspected of suboptimal effort in the domain of executive functioning. Further research is needed to establish whether the MET-SV can be reliably used in medico-legal settings.

Short-term outcome of periviable small-for-gestational-age babies: is our counseling up to date?

There are limited data for counseling on and management of periviable small‐for‐gestational‐age (SGA) fetuses. We therefore aimed to investigate the short‐term outcome of periviable SGA fetuses in relation to the likely underlying cause.

Fetal Skull Segmentation in 3D Ultrasound via Structured Geodesic Random Forest

Ultrasound is the primary imaging method for prenatal screening and diagnosis of fetal anomalies. Thanks to its non-invasive and non-ionizing properties, ultrasound allows quick, safe and detailed evaluation of the unborn baby, including the estimation of the gestational age, brain and cranium development. However, the accuracy of traditional 2D fetal biometrics is dependent on operator expertise and subjectivity in 2D plane finding and manual marking. 3D ultrasound has the potential to reduce the operator dependence. In this paper, we propose a new random forest-based segmentation framework for fetal 3D ultrasound volumes, able to efficiently integrate semantic and structural information in the classification process. We introduce a new semantic features space able to encode spatial context via generalized geodesic distance transform. Unlike alternative auto-context approaches, this new set of features is efficiently integrated into the same forest using contextual trees. Finally, we use a new structured labels space as alternative to the traditional atomic class labels, able to capture morphological variability of the target organ. Here, we show the potential of this new general framework segmenting the skull in 3D fetal ultrasound volumes, significantly outperforming alternative random forest-based approaches.

An ergonomic handheld ultrasound probe providing contact forces and pose information.

This paper presents a handheld ultrasound probe which is integrated with sensors to measure force and pose (position/orientation) information. Using an integrated probe like this, one can relate ultrasound images to spatial location and create 3D ultrasound maps. The handheld device can be used by sonographers and also easily be integrated with robot arms for automated sonography. The handheld device is ergonomically designed; rapid attachment and removal of the ultrasound transducer itself is possible using easy-to-operate clip mechanisms. A cable locking mechanism reduces the impact that gravitational and other external forces have (originating from data and power supply cables connected to the probe) on our measurements. Gravitational errors introduced by the housing of the probe are compensated for using knowledge of the housing geometry and the integrated pose sensor that provides us with accurate orientation information. In this paper, we describe the handheld probe with its integrated force/pose sensors and our approach to gravity compensation. We carried out a set of experiments to verify the feasibility of our approach to obtain accurate spatial information of the handheld probe.

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network

We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location. PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume. Our approach adopts a multi-task learning framework that combines regression and classification to improve localisation accuracy. We extend PIN to localise multiple landmarks by using principal component analysis, which models the global anatomical relationships between landmarks. We have evaluated PIN using 72 3D ultrasound images from fetal screening examinations. PIN achieves quantitatively an average landmark localisation error of 5.59mm and a runtime of 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2D standard scan planes derived from the predicted landmark locations are visually similar to the clinical ground truth. Source code is publicly available at this https URL.

Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83 mm/12.7(^{circ }) and 3.80 mm/12.6(^{circ }) for the transventricular and transcerebellar planes respectively and takes 0.46 s per plane.

Short term outcome of Periviable SGA: Is our counseling up to date?

There are limited data for counseling on and management of periviable small‐for‐gestational‐age (SGA) fetuses. We therefore aimed to investigate the short‐term outcome of periviable SGA fetuses in relation to the likely underlying cause.

Short term outcome of Periviable SGA

There are limited data for counseling on and management of periviable small‐for‐gestational‐age (SGA) fetuses. We therefore aimed to investigate the short‐term outcome of periviable SGA fetuses in relation to the likely underlying cause.