Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: A machine learning approach

Background: Identifying candidates for left ventricular assist device surgery at risk of right ventricular failure remains difficult. The aim was to identify the most accurate predictors of right ventricular failure among clinical, biological, and imaging markers, assessed by agreement of different supervised machine learning algorithms. Methods: Seventy-four patients, referred to HeartWare left ventricular assist device since 2010 in two Italian centers, were recruited. Biomarkers, right ventricular standard, and strain echocardiography, as well as cath-lab measures, were compared among patients who did not develop right ventricular failure (N = 56), those with acute–right ventricular failure (N = 8, 11%) or chronic–right ventricular failure (N = 10, 14%). Logistic regression, penalized logistic regression, linear support vector machines, and naïve Bayes algorithms with leave-one-out validation were used to evaluate the efficiency of any combination of three collected variables in an “all-subsets” approach. Results: Michigan risk score combined with central venous pressure assessed invasively and apical longitudinal systolic strain of the right ventricular–free wall were the most significant predictors of acute–right ventricular failure (maximum receiver operating characteristic–area under the curve = 0.95, 95% confidence interval = 0.91–1.00, by the naïve Bayes), while the right ventricular–free wall systolic strain of the middle segment, right atrial strain (QRS-synced), and tricuspid annular plane systolic excursion were the most significant predictors of Chronic-RVF (receiver operating characteristic–area under the curve = 0.97, 95% confidence interval = 0.91–1.00, according to naïve Bayes). Conclusion: Apical right ventricular strain as well as right atrial strain provides complementary information, both critical to predict acute–right ventricular failure and chronic–right ventricular failure, respectively

Differential diagnosis of syndromic craniosynostosis: a case series

Purpose: Syndromic craniosynostosis is a rare genetic disease caused by premature fusion of one or multiple cranial sutures combined with malformations of other organs. The aim of this publication is to investigate sonographic signs of different syndromic craniosynostoses and associated malformations to facilitate a precise and early diagnosis. Methods: We identified in the period of 2000-2019 thirteen cases with a prenatal suspected diagnosis of syndromic craniosynostosis at our department. We analyzed the ultrasound findings, MRI scans, genetic results as well as the mode of delivery, and postnatal procedures. Results: Eight children were diagnosed with Apert Syndrome, two with Saethre Chotzen syndrome, one with Crouzon syndrome, and one with Greig cephalopolysyndactyly syndrome. One child had a mutation p.(Pro253Leu) in the FGFR2 gene. We identified characteristic changes of the head shape as well as typical associated malformations. Conclusion: Second trimester diagnosis of syndromic craniosynostosis is feasible based on the identified sonographic signs. In case of a suspected diagnosis a genetic, neonatal as well as surgical counseling is recommended. We also recommend to offer a fetal MRI. The delivery should be planned in a perinatal center

Study Protocol of the Exercise Study:Unraveling Limitations for Physical Activity in Children With Chronic Diseases in Order to Target Them With Tailored Interventions—A Randomized Cross Over Trial

Introduction: Physical activity is associated with many physiological and psychological health benefits across the lifespan. Children with a chronic disease often have lower levels of daily physical activity, and a decreased exercise capacity compared to healthy peers. In order to learn more about limitations for physical activity, we investigate children with four different chronic diseases: children with a Fontan circulation, children with Broncho Pulmonary Dysplasia (BPD), Pompe disease and inflammatory bowel disease (IBD). Each of these diseases is likely to interfere with physical activity in a different way. Knowing the specific limitations for physical activity would make it possible to target these, and increase physical activity by a personalized intervention. The aim of this study is to first investigate limitations for physical activity in children with various chronic diseases. Secondly, to measure the effects of a tailored exercise intervention, possibly including a personalized dietary advice and/or psychological counseling, on exercise capacity, endurance, quality of life, fatigue, fear for exercise, safety, muscle strength, physical activity levels, energy balance, and body composition. Methods and Analysis: This randomized crossover trial will aim to include 72 children, aged 6–18 years, with one of the following diagnosis: a Fontan circulation, BPD, Pompe disease and IBD. Eligible patients will participate in the 12-week tailored exercise intervention and are either randomized to start with a control period or start with the intervention. The tailored 12-week exercise interventions, possibly including a personalized dietary advice and/or psychological counseling, will be designed based on the found limitations for physical activity in each disease group during baseline measurements by the Rotterdam Exercise Team. Effects of the tailored training interventions will be measured on the following endpoints: exercise capacity (measured by cardiopulmonary exercise test), endurance, physical activity levels, muscle strength, quality of life, fatigue, fear for exercise, disease activity, cardiac function (in children with a Fontan circulation), energy balance, and body composition. Ethics and Dissemination: Conducted according to the Declaration of Helsinki and Good Clinical Practice. Medical-ethical approval was obtained. Trial Registration Number: NL8181, https://www.trialregister.nl/trial/8181.</p

An Affordable Portable Obstetric Ultrasound Simulator for Synchronous and Asynchronous Scan Training

The increasing use of Point of Care (POC) ultrasound presents a challenge in providing efficient training to new POC ultrasound users. In response to this need, we have developed an affordable, compact, laptop-based obstetric ultrasound training simulator. It offers freehand ultrasound scan on an abdomen-sized scan surface with a 5 degrees of freedom sham transducer and utilizes 3D ultrasound image volumes as training material. On the simulator user interface is rendered a virtual torso, whose body surface models the abdomen of a particular pregnant scan subject. A virtual transducer scans the virtual torso, by following the sham transducer movements on the scan surface. The obstetric ultrasound training is self-paced and guided by the simulator using a set of tasks, which are focused on three broad areas, referred to as modules: 1) medical ultrasound basics, 2) orientation to obstetric space, and 3) fetal biometry. A learner completes the scan training through the following three steps: (i) watching demonstration videos, (ii) practicing scan skills by sequentially completing the tasks in Modules 2 and 3, with scan evaluation feedback and help functions available, and (iii) a final scan exercise on new image volumes for assessing the acquired competency. After each training task has been completed, the simulator evaluates whether the task has been carried out correctly or not, by comparing anatomical landmarks identified and/or measured by the learner to reference landmark bounds created by algorithms, or pre-inserted by experienced sonographers. Based on the simulator, an ultrasound E-training system has been developed for the medical practitioners for whom ultrasound training is not accessible at local level. The system, composed of a dedicated server and multiple networked simulators, provides synchronous and asynchronous training modes, and is able to operate with a very low bit rate. The synchronous (or group-learning) mode allows all training participants to observe the same 2D image in real-time, such as a demonstration by an instructor or scan ability of a chosen learner. The synchronization of 2D images on the different simulators is achieved by directly transmitting the position and orientation of the sham transducer, rather than the ultrasound image, and results in a system performance independent of network bandwidth. The asynchronous (or self-learning) mode is described in the previous paragraph. However, the E-training system allows all training participants to stay networked to communicate with each other via text channel. To verify the simulator performance and training efficacy, we conducted several performance experiments and clinical evaluations. The performance experiment results indicated that the simulator was able to generate greater than 30 2D ultrasound images per second with acceptable image quality on medium-priced computers. In our initial experiment investigating the simulator training capability and feasibility, three experienced sonographers individually scanned two image volumes on the simulator. They agreed that the simulated images and the scan experience were adequately realistic for ultrasound training; the training procedure followed standard obstetric ultrasound protocol. They further noted that the simulator had the potential for becoming a good supplemental training tool for medical students and resident doctors. A clinic study investigating the simulator training efficacy was integrated into the clerkship program of the Department of Obstetrics and Gynecology, University of Massachusetts Memorial Medical Center. A total of 24 3rd year medical students were recruited and each of them was directed to scan six image volumes on the simulator in two 2.5-hour sessions. The study results showed that the successful scan times for the training tasks significantly decreased as the training progressed. A post-training survey answered by the students found that they considered the simulator-based training useful and suitable for medical students and resident doctors. The experiment to validate the performance of the E-training system showed that the average transmission bit rate was approximately 3-4 kB/s; the data loss was less than 1% and no loss of 2D images was visually detected. The results also showed that the 2D images on all networked simulators could be considered to be synchronous even though inter-continental communication existed

Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives

Ultrasound (US) is one of the most widely used modalities for clinical intervention and diagnosis due to the merits of providing non-invasive, radiation-free, and real-time images. However, free-hand US examinations are highly operator-dependent. Robotic US System (RUSS) aims at overcoming this shortcoming by offering reproducibility, while also aiming at improving dexterity, and intelligent anatomy and disease-aware imaging. In addition to enhancing diagnostic outcomes, RUSS also holds the potential to provide medical interventions for populations suffering from the shortage of experienced sonographers. In this paper, we categorize RUSS as teleoperated or autonomous. Regarding teleoperated RUSS, we summarize their technical developments, and clinical evaluations, respectively. This survey then focuses on the review of recent work on autonomous robotic US imaging. We demonstrate that machine learning and artificial intelligence present the key techniques, which enable intelligent patient and process-specific, motion and deformation-aware robotic image acquisition. We also show that the research on artificial intelligence for autonomous RUSS has directed the research community toward understanding and modeling expert sonographers' semantic reasoning and action. Here, we call this process, the recovery of the "language of sonography". This side result of research on autonomous robotic US acquisitions could be considered as valuable and essential as the progress made in the robotic US examination itself. This article will provide both engineers and clinicians with a comprehensive understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi