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Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/., Comment: in IEEE Transactions on Biomedical Engineering

Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

Maximal oxygen uptake (VO2max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO2max, and of increases of VO2max in response to exercise training (ΔVO2max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO2max and ΔVO2max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual’s ΔVO2max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans. Plasma proteomic profiles from 650 adult humans are measured before and after a 20-week exercise regimen to determine proteins associated with baseline cardiorespiratory fitness and improvements in response to exercise.

Zusammenfassung Ziel der Studie Zahlreiche Studien weisen auf ein breites Spektrum häufig gleichzeitig auftretender psychischer Störungen wie jene der Generalisierten Angststörung bei der Behandlung von Personen mit Cannabiskonsumstörungen hin (GAD). Bisherige Internet-basierte Studien konnten zwar vereinzelt die Reduktion von Cannabiskonsum zeigen, berücksichtigten aber keine komorbide Störungen. Ziel des E-Coach gestützten Online-Selbsthilfeprogramm CANreduce 2.0 ist die Reduzierung des Cannabiskonsums und der gegebenenfalls vorhandenen GAD. Methodik Sekundäranalyse der in der Allgemeinbevölkerung rekrutierten Cannabiskonsumierenden der CANreduce 2.0 Studie der beiden aktiven Interventionsgruppen (N=367). Für die Analysen wurden diese Teilnehmende aufgrund ihres erreichten Symptomatik-Wertes der General Anxiety Disorder Scale (GAD-7) in 4 Gruppen eingeteilt: Minimale (GAD-7 Werte: 0–4; N=102), Milde (5–9; N=151), Moderate (10–14; N=78) und Schwere (15–21; N=36) Angststörung und anschliessend ihre Behandlungsverläufe mittels Intention-to-Treat Analysen bezüglich ihrer Cannabiskonsumtage und ihrer Werte auf dem Cannabis Use Disorder Identification Test (CUDIT), der Severity of Dependence Scale (SDS), der GAD-7, der Center of Epidemiologic Studies of Depression Scale (CES-D) sowie bezüglich ihrer Adhärenz verglichen. Ergebnisse Alle 4 Gruppen reduzierten unabhängig von ihrer GAD-Symptomatik die Anzahl ihrer Cannabiskonsumtage, ihren CUDIT und SDS Score sowohl 6 Wochen als auch 3 Monate nach der Baselineerhebung. Weiter haben mit Ausnahme von der minimalen GAD-7-Gruppe alle Gruppen eine Reduktion der Angstsymptomatik nach 3 Monaten aufgewiesen. Analog zeigten mit Ausnahme der minimalen GAD-7-Gruppe alle GAD-7-Gruppen eine deutliche Reduktion in der depressiven Symptomatik. Bezüglich der gemessenen Adhärenz zeigten sich keine Unterschiede zwischen den GAD-7-Gruppen. Schlussfolgerungen CANreduce 2.0 scheint sowohl den Cannabiskonsum, die Ausprägung der Cannabiskonsumstörung bzw. die Cannabisabhängigkeit wie auch eine gegebenenfalls vorhandene GAD-Symptomatik zu reduzieren.

ZusammenfassungZiel Untersuchung von Zusammenhängen zwischen der Haptoglobinkonzentration im Blut und dem Auftreten einer Ketose, ausgewählten klinischen Parametern und Lahmheit bei Milchkühen.Material und Methoden Die Datenerhebung fand in 39 bayerischen Milchviehbetrieben über 8 Monate statt. Bei den einbezogenen 712 Fleckvieh- und Braunviehkühen, deren Kalbung 10–30 Tage zurücklag, wurde eine klinische Untersuchung durchgeführt sowie eine Milch- und eine Blutprobe entnommen. Im Blut wurden die Konzentrationen von freien Fettsäuren (non-esterified fatty acids, NEFA), β-Hydroxybutyrat (BHB) und Haptoglobin (Hp) bestimmt, in der Milch die Milchinhaltsstoffe (Fett, Eiweiß, Harnstoff, Laktose, Azeton), BHB, NEFA und die somatische Zellzahl (SCC).Ergebnisse Signifikante Korrelationen ergaben sich zwischen erhöhten Hp-Konzentrationen einerseits und erhöhten NEFA-Konzentrationen in Blut und Milch (p < 0,001), erhöhter SCC (p < 0,001), Lahmheit (p < 0,001), vermindertem Laktosegehalt (p < 0,001) sowie vermindertem Proteingehalt der Milch (p = 0,001) andererseits. In den wärmeren Sommermonaten beprobte Tiere hatten signifikant höhere Hp-Konzentrationen (p < 0,001). Erstkalbinnen wiesen signifikant höhere Hp-Werte auf als multipare Tiere (p < 0,001). Durch die Einteilung der Tiere in 4 Cluster ließ sich ein Hp-Grenzwert bestimmen, der 0,18 mg/ml betrug. Kombiniert mit einem SCC-Grenzwert von 40 500 Zellen/ml Milch konnte der Großteil der subklinisch und klinisch auffälligen Tiere ermittelt werden.Schlussfolgerung und klinische Relevanz Die Messung der Hp-Konzentration im Blut stellt einen praktikablen Ansatz zur Unterstützung des Tiergesundheitsmonitorings im postpartalen Zeitraum dar. Zusammen mit der Auswertung der Milchmenge und der Milchinhaltsstoffe können Abweichungen von physiologischen Zuständen erkannt und betroffene Tiere zeitnah behandelt werden. Haptoglobin kann für die Bewertung des Gesundheitsstatus des Einzeltieres wie auch als Indikator für die Herdengesundheit genutzt werden.

Author(s): Hsu, William; Baumgartner, Christian; Deserno, Thomas M; Section Editors for the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics | Abstract: ObjectiveTo highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics.MethodA broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers.ResultsThe query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals.ConclusionsThe four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

Aims: Modern pharmaceutical product development is a long and complex process associated with significant investments by pharmaceutical companies. The innovative pharmaceutical industry accounts for the vast majority of expenditures in clinical trials of potential new pharmaceuticals and therefore generates economic activity within a country. The aim was to assess the far-reaching economic impact of industry-sponsored clinical-trials (ISCTs) of pharmaceutical products for the healthcare system and the national economy. Materials and methods: The study approach was based on three analytical steps. First, a survey among 15 pharmaceutical companies in Austria was conducted to evaluate the annual number of ISCTs subdivided according to trial phase, therapeutic areas and associated employees. Second, the monetary value of treatments performed in ISCTs was calculated based on a sample of clinical-trial protocols. Finally, the macroeconomic impact, measured in terms of value-added and jobs created by the conducted ISCTs, was calculated using Input–Output analysis by applying an extended Leontief-model. Results: The study demonstrated that €116.22 million spent in ISCTs generated a total value added of €144 million, €74 million direct, in 2018. Each year a medical treatment value of €100 million was financed through 463 ISCTs, with an average value of medical treatment of €37,068 per recruited patient. This represents a significant 0.3% of annual current health-expenditures. In summary, each Euro invested by the pharmaceutical industry in ISCTs generates €1.95 for the Austrian economy. ISCTs also created and secured employment in the extent of 2,021 full-time-equivalents, thus resulting in an employment multiplier of 1.66. Conclusions: In conclusion, conducting clinical-trials by pharmaceutical industry—beside its importance in its own domain—results in tangible benefits and a positive macroeconomic impact that contribute to the sustainability of the Austrian healthcare system by complementing its limited resources. Furthermore, it is a non-negligible factor in locational and industrial policy.

Highlights • Cognitive-behavioural therapy (CBT) and motivational interviewing (MI) can be readily adapted for brief, minimally guided, online interventions. • Minimally guided online treatment is beneficial for hazardous drinking, mood difficulties, and quality of life in the short-term among young adults. • Strategies designed to increase treatment engagement will likely mitigate the risk of attrition in future programs. • Future versions of the Take Care of Me program are still needed to determine long-term effects and differential responses to treatment., Objective Many young adults struggle with comorbid alcohol misuse and emotional problems (i.e., depression and anxiety). However, there is currently a paucity of evidence-based, integrated, accessible treatment options for individuals with these comorbidities. The main goal of this study was to examine efficacy of a novel online, minimally guided, integrated program for comorbid alcohol misuse and emotional problems in young adults. Method: The study was an open-label two-arm RCT. Participants (N = 222, Mage = 24.6, 67.6% female) were randomized to one of two conditions: the Take Care of Me program (an 8-week, online integrated treatment condition consisting of 12 modules), or an online psychoeducational control condition. Intervention modules incorporated content based on principles of cognitive behavioral therapy and motivational interviewing. Participants completed assessment data at baseline, at the end of treatment (i.e., 8 weeks), and at follow-up (i.e., 24 weeks). Data were analyzed using generalized linear mixed models. Results: We observed that participants in the treatment condition showed larger reductions in depression, hazardous drinking, as well as increases in psychological quality of life and confidence at the end of treatment. We did not find group differences on total alcohol use at follow-up, but participants in the treatment group reduced their hazardous drinking and improved their quality of life at 24-week follow-up. Conclusions: Our study provides promising initial evidence for the first iteration of the comorbid alcohol misuse and emotional problems online program.

Over the past decade, deep learning has become the gold standard for automatic medical image segmentation. Every segmentation task has an underlying uncertainty due to image resolution, annotation protocol, etc. Therefore, a number of methods and metrics have been proposed to quantify the uncertainty of neural networks mostly based on Bayesian deep learning, ensemble learning methods or output probability calibration. The aim of our research is to assess how reliable the different uncertainty metrics found in the literature are. We propose a quantitative and statistical comparison of uncertainty measures based on the relevance of the uncertainty map to predict misclassification. Four uncertainty metrics were compared over a set of 144 models. The application studied is the segmentation of the lumen and vessel wall of carotid arteries based on multiple sequences of magnetic resonance (MR) images in multi-center data.

Background Demographic changes, increased life expectancy and the associated rise in chronic diseases pose challenges to public health care systems. Optimized treatment methods and integrated concepts of care are potential solutions to overcome increasing financial burdens and improve quality of care. In this context modeling is a powerful tool to evaluate potential benefits of different treatment procedures on health outcomes as well as health care budgets. This work presents a novel modeling approach for simulating different treatment procedures of heart failure patients based on extensive data sets from outpatient and inpatient care. Methods Our hybrid heart failure model is based on discrete event and agent based methodologies and facilitates the incorporation of different therapeutic procedures for outpatient and inpatient care on patient individual level. The state of health is modeled with the functional classification of the New York Heart Association (NYHA), strongly affecting discrete state transition probabilities alongside age and gender. Cooperation with Austrian health care and health insurance providers allowed the realization of a detailed model structure based on clinical data of more than 25,000 patients. Results Simulation results of conventional care and a telemonitoring program underline the unfavorable prognosis for heart failure and reveal the correlation of NYHA classes with health and economic outcomes. Average expenses for the treatment of NYHA class IV patients of €10,077 ± €165 were more than doubled compared to other classes. The selected use case of a telemonitoring program demonstrated potential cost savings within two years of application. NYHA classes II and III revealed most potential for additional treatment measures. Conclusion The presented model allows performing extensive simulations of established treatment procedures for heart failure patients and evaluating new holistic methods of care and innovative study designs. This approach offers health care providers a unique, adaptable and comprehensive tool for decision making in the complex and socioeconomically challenging field of cardiovascular diseases.

Due to globalization and the quick development of technology, each government aims to ensure the safety and performance of products brought to their markets to protect its population. The peoples’ health state is of great significance and influenced easily by the quality of medical products. Therefore governments enact laws, directives and regulations to assure that quality. Nevertheless, these regulations can impede innovation and create trade barriers which result in an adverse effect on national economies. Where in some states no regulatory system is installed, others have highly sophisticated registration requirements that must be met. These diversities result in a challenge especially for small and medium sized companies, whose resources are often limited. [1] Manufacturers must control these diverse regulatory requirements by analyzing each market. Therefore an efficient procedure of registration should be defined that streamlines different registration requirements and ensures regulatory compliance. Next to regulatory authorities describing these requirements also harmonization groups play an important role in the design of the global regulatory landscape. Therefore also the most important harmonization groups and their current activities were investigated. The analysis showed that the impact of harmonization groups can be identified easily since new defined or updated regulatory requirements are oriented on their guideline. Still the harmonization of regulatory requirements in the medical device sector is not sufficiently enhanced. Thus the confrontation with each national regulatory system is inevitable.

Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multicoil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions., IEEE Transactions on Medical Imaging, 38 (7), ISSN:0278-0062, ISSN:1558-254X

Quantifying uncertainty in medical image segmentation applications is essential, as it is often connected to vital decision-making. Compelling attempts have been made in quantifying the uncertainty in image segmentation architectures, e.g. to learn a density segmentation model conditioned on the input image. Typical work in this field restricts these learnt densities to be strictly Gaussian. In this paper, we propose to use a more flexible approach by introducing Normalizing Flows (NFs), which enables the learnt densities to be more complex and facilitate more accurate modeling for uncertainty. We prove this hypothesis by adopting the Probabilistic U-Net and augmenting the posterior density with an NF, allowing it to be more expressive. Our qualitative as well as quantitative (GED and IoU) evaluations on the multi-annotated and single-annotated LIDC-IDRI and Kvasir-SEG segmentation datasets, respectively, show a clear improvement. This is mostly apparent in the quantification of aleatoric uncertainty and the increased predictive performance of up to 14%. This result strongly indicates that a more flexible density model should be seriously considered in architectures that attempt to capture segmentation ambiguity through density modeling. The benefit of this improved modeling will increase human confidence in annotation and segmentation, and enable eager adoption of the technology in practice.

Objective To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. Methods 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). Results Automated segmentations showed high agreement (DSC 0.89-0.92) and high correlations (r >= 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (, Magnetic Resonance Materials in Physics, Biology, and Medicine, 34 (3), ISSN:0968-5243, ISSN:1352-8661

Background Gambling and tobacco smoking are highly comorbid among North American adults. However, there is a paucity of treatment options that are integrated (i.e. targeting both gambling and tobacco smoking simultaneously), accessible, and evidence based. Methods The aim of this two-arm open-label randomized controlled trial is to examine the effectiveness of an online, self-guided integrated treatment for problem gambling and tobacco smoking. A target sample of 214 participants will be recruited and be randomized into either an 8-week integrated or gambling only control condition. Both conditions will consist of seven online modules following cognitive behavioural therapy and motivational interviewing principles. Our three primary outcomes are (1) the number of days gambled, (2) money spent on gambling activities, and (3) time spent in gambling activities. Secondary outcomes include gambling disorder symptoms, cigarette use, and nicotine dependence symptoms. Assessments will be completed at baseline, at completion (i.e. 8 weeks from baseline), and at follow-up (i.e. 24 weeks from baseline). Generalized linear mixed modelling will be used to evaluate our primary and secondary outcomes. We expect that participants receiving online integrated treatment will show larger reductions in gambling relative to those receiving a control gambling only intervention. We further hypothesize that reductions in smoking will mediate these group differences. Discussion The rates of problem gambling and tobacco smoking are high in North America; yet, the treatment options for both are limited, with no integrated treatments available. If supported, our pilot study will be a cost-effective and accessible way to improve treatments for co-occurring problem gambling and tobacco use. Trial registration ClinicalTrials.gov NCT03614884. Registered on August 3, 2019

There is recent experimental evidence that sarcomere strain behavior is highly heterogeneous across and between muscle structures. Hence, considering the hierarchical architecture of muscles, also the behavior of all acting muscle substructures is effected. However, typical investigations are limited to ex-vivo experiments or pose serious limitations for in-vivo studies. In this work, we investigate in-vivo length changes in human muscle fascicles by means of non-invasive ultrafast ultrasound. To this end, we employ a research ultrasound system and a linear array transducer to image, in plane-wave mode, medial gastrocnemius muscles during electrically-stimulated contractions. The ultrasound-based approach presented in this study allows to measure strain distributions within muscle fascicles during contractions, where the sub-fascicle structures exhibit heterogeneous behaviors.

Until now direct localization of motor endplates for any arbitrary muscle structure was reserved to cadaver studies or restricted to specific muscles in-vivo. Utilizing ultrafast ultrasound we report a versatile in-vivo method for the detection of motor endplate zones. To achieve this goal, we employ a research ultrasound system with a linear array transducer probe to record ultrasound images of electrically-stimulated medial gastrocnemius muscles. An optical flow speckle tracking algorithm is used to track the points along a single fascicle. We localize motor endplates at intersection points where mechanical contraction waves diverge bidirectionally towards the fiber ends. Contrary to common experimental methods, our proposed ultrafast ultrasound-based approach allows to localize motor endplates also in deep and pennate muscles in-vivo. These results open up new applications for ultrasound in neuromuscular research.

The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a "clinical level" study. Almost 39% of the articles achieved a "robust candidate" and as many as 61% a "proof of concept" status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.

Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. Materials and methods The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. Results The segmentation time of the method is, Magnetic Resonance Materials in Physics, Biology, and Medicine, 33, ISSN:0968-5243, ISSN:1352-8661

Along with increasing enthusiasm for sports comes an increase of sport related injuries. One of the most common injuries in the human knee is the tear of the anterior cruciate ligament (ACL). The selection of a graft fixation device is an important factor that determines the outcome of an anterior cruciate ligament reconstruction. Before the healing process is completed, the graft is dependent on tibial and femoral fixation devices to maintain normal ACL graft tension. Among various devices, the use of an adjustable loop suspensory fixation device (ALD) in soft-tissue graft reconstruction attracts current interest. An advantage of the ALD is the ability to draw the graft to the depth of the bone tunnel to achieve adequate graft tension while minimizing the empty space in the tunnel. In this study a comprehensive controlled laboratory investigation is performed to examine the biomechanical properties of commonly used cortical fixation devices, with the aim of implementing a standard testing procedure for adjustable loop devices. The procedure consists of three test series, a loop shortening test and two different stability test series (singe device and tendon device test). Those test series are used to compare the performance of a new ALD from Arthrex (Naples, USA) with five competitor devices already on the market. In order to obtain representative results eight samples of each device are tested. In comparison to the previously performed studies, a complete unloading is applied in the stability tests, which allows for a detailed examination of the ALDs locking mechanisms in dynamically loaded test situations. Furthermore, the performed loop shortening tests reveal important aspects, such as the shortening accuracy and settling effects of the loops, that are not found in previous studies. Therefore, the used test protocol can be recommended for further testing.

Objective: To identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics. Method: A broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered. Results: A search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions: The fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models.

Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different styles of annotating. The majority of current state-of-the-art methods do not account for such ambiguities but rather learn a single mapping from image to segmentation. In this work, we propose a novel method to model the conditional probability distribution of the segmentations given an input image. We derive a hierarchical probabilistic model, in which separate latent variables are responsible for modelling the segmentation at different resolutions. Inference in this model can be efficiently performed using the variational autoencoder framework. We show that our proposed method can be used to generate significantly more realistic and diverse segmentation samples compared to recent related work, both, when trained with annotations from a single or multiple annotators., Comment: Accepted to MICCAI 2019

Background Given the scarcity of alcohol prevention and use disorder treatments in many low- and middle-income countries (LMICs), the World Health Organization has launched an eHealth portal that includes the web-based self-help program “Alcohol e-Health.” Objective We aimed to test the effectiveness of the Alcohol e-Health program in a randomized controlled trial. Methods This was a two-arm, individually randomized, and controlled trial across four LMICs comparing the self-help program and a psychoeducation and internet access as usual waiting list. Participants were broadly recruited from community samples in Belarus, Brazil, India, and Mexico from January 2016 through January 2019. The primary outcome measure was change in the Alcohol Use Disorders Identification Test (AUDIT) score with a time frame of 6 months between baseline and follow-up. Secondary outcomes included self-reported numbers of standard drinks over the previous week and cessation of harmful or hazardous drinking (AUDIT score Results For this study, we recruited 1400 predominantly male (n=982, 70.1%) at least harmful or hazardous alcohol drinkers. The mean age was 37.6 years (SD 10.5). The participants were recruited from Brazil (n=587), Mexico (n=509), India (n=212), and Belarus (n=92). Overall, complete case analysis identified higher AUDIT changes in the intervention group (B=−4.18, 95% CI −5.42 to −2.93, P Conclusions The expansion of the Alcohol e-Health program to other LMICs with underdeveloped alcohol prevention and treatment systems for alcohol use disorders should be considered after successful replication of the present results. Trial Registration ISRCTN ISRCTN14037475; https://www.isrctn.com/ISRCTN14037475 International Registered Report Identifier (IRRID) RR2-10.1111/add.14034

Background Depression and harmful alcohol use are two of the top five leading causes of years of life lost to disability in high-income countries. Integrated treatment targeting both at the same time is often considered more complicated and difficult and, therefore, more expensive. Consequently, integrated internet-based interventions could be a valuable addition to traditional care. Methods A three-arm randomized controlled trial was conducted comparing the effectiveness of (1) an integrated, minimal-guidance, adherence-focused self-help intervention designed to reduce both alcohol use and depression symptoms (AFGE-AD); (2) a similar intervention designed to reduce alcohol use only (AFGE-AO), and (3) internet access as usual (IAU) as a control condition, in at least moderately depressed alcohol misusers from February 2016—March 2020. We recruited 689 alcohol misusers (51.6 % males, mean age = 42.8 years) with at least moderate depression symptoms not otherwise in treatment from the general population. Six months after baseline, 288 subjects (41.8 %) were reachable for the final assessment. Results All interventions yielded reduced alcohol-use after six months (AFGE-AD: -16.6; AFGE-AO: -19.8; IAU: -13.2). Those who undertook active-interventions reported significantly fewer standard drinks than controls (AFGE-AD: p = .048, d=0.10; AFGE-AO: p = .004, d=0.20). The two active-intervention groups also reported significantly less severe depression symptoms than controls (AFGE-AD: p = .006, d=0.41; AFGE-AO: p = .008, d=0.43). Testing revealed noninferiority between the two interventions. Conclusions This study documented sustained effectiveness of the first integrated, fully internet-based self-help intervention developed for the reduction of both alcohol use and depression symptoms in at least moderately depressed adult alcohol misusers recruited from the general population.

Identification of the best CMR technique for quantitative assessment of myocardial salvage using a systematic comparison.

Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55$\pm$1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use via: https://github.com/luuleitner/deepMTJ, Comment: Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada

We present simultaneous measurements of Josephson inductance and DC transport characteristics of ballistic Josephson junctions based upon an epitaxial Al-InAs heterostructure. The Josephson inductance at finite current bias directly reveals the current-phase relation. The proximity-induced gap, the critical current and the average value of the transparency $\bar{\tau}$ are extracted without need for phase bias, demonstrating, e.g.,~a near-unity value of $\bar{\tau}=0.94$. Our method allows us to probe the devices deeply in the non-dissipative regime, where ordinary transport measurements are featureless. In perpendicular magnetic field the junctions show a nearly perfect Fraunhofer pattern of the critical current, which is insensitive to the value of $\bar{\tau}$. In contrast, the signature of supercurrent interference in the inductance turns out to be extremely sensitive to $\bar{\tau}$., Comment: 13 pages, 13 figures

Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen images. In practice, obtaining a large number of annotations from clinical experts is expensive and time-consuming. One way to address scarcity of annotated examples is data augmentation using random spatial and intensity transformations. Recently, it has been proposed to use generative models to synthesize realistic training examples, complementing the random augmentation. So far, these methods have yielded limited gains over the random augmentation. However, there is potential to improve the approach by (i) explicitly modeling deformation fields (non-affine spatial transformation) and intensity transformations and (ii) leveraging unlabelled data during the generative process. With this motivation, we propose a novel task-driven data augmentation method where to synthesize new training examples, a generative network explicitly models and applies deformation fields and additive intensity masks on existing labelled data, modeling shape and intensity variations, respectively. Crucially, the generative model is optimized to be conducive to the task, in this case segmentation, and constrained to match the distribution of images observed from labelled and unlabelled samples. Furthermore, explicit modeling of deformation fields allow synthesizing segmentation masks and images in exact correspondence by simply applying the generated transformation to an input image and the corresponding annotation. Our experiments on cardiac magnetic resonance images (MRI) showed that, for the task of segmentation in small training data scenarios, the proposed method substantially outperforms conventional augmentation techniques., 13 pages, 3 figures, 1 table, This article has been accepted at the 26th international conference on Information Processing in Medical Imaging (IPMI) 2019

Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of annotated samples from experts is time-consuming and expensive. Consequently, numerous methods have been proposed in the literature for learning with limited annotated examples. Unfortunately, the proposed approaches in the literature have not yet yielded significant gains over random data augmentation for image segmentation, where random augmentations themselves do not yield high accuracy. In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task. The generator of the proposed method models intensity and shape variations using two sets of transformations, as additive intensity transformations and deformation fields. Both transformations are optimized using labeled as well as unlabeled examples in a semi-supervised framework. Our experiments on three medical datasets, namely cardiac, prostate and pancreas, show that the proposed approach significantly outperforms standard augmentation and semi-supervised approaches for image segmentation in the limited annotation setting. The code is made publicly available at https://github.com/krishnabits001/task_driven_data_augmentation., Medical Image Analysis, 68, ISSN:1361-8415, ISSN:1361-8423

Objective Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support. Methods Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab ® —a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated. Results The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4–6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1–4.6). Discussion Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab ® , user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant. Conclusion Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia.

IntroductionThe past-year prevalence of problem gambling worldwide averages 2.3%. Switzerland exhibits a slightly lower past-year prevalence rate, of 1.1%, among adults. Only a minority of these adults attend outpatient treatment. Surveyed problem gamblers have explained that they wanted to handle the problem on their own. The option of a web-based self-help programme could potentially reach those users who hesitate to approach treatment centres and help them to reduce or stop their problem gambling. The effectiveness of such web-based interventions has been shown in other countries.Methods and analysisThis two-armed randomised controlled trial (RCT) will examine the efficacy of a web-based self-help intervention, relative to an active control condition with a self-help manual, at reducing problem gambling. The active intervention programme, spanning 8 weeks, consists of nine modules developed to reduce gambling and attenuate psychopathological comorbidity, including depression, anxiety and stress-related disorder symptoms, relying on motivational interviewing and cognitive behavioural therapy. With a target sample size of 352, questionnaire data will be collected at baseline, and at 8 and 24 weeks after baseline. Primary outcomes will be the number of days one has gambled in the last 30 days. Secondary outcomes will include money and time spent on gambling activities, changes in gambling-related problems (Problem Gambling Severity Index, Gambling Symptom Assessment Scale), use of alcohol and cigarettes, and psychopathological comorbidity. All data analysis will comply with the intention-to-treat principle.Ethics and disseminationThe RCT will be conducted in accordance with the Declaration of Helsinki; the consort eHealth Guidelines for studies on medical devices; the European Directive on medical devices 93/42/EEC, Swiss Law and Swiss Regulatory Authority requirements. The study was approved by the ethics committee of the Canton of Zurich. Results will be published in a scientific peer-reviewed journal. Participants will be informed via e-mail about study results via a lay-person-friendly summary of trial findings.Trial registration numberCurrent Controlled Trials registry (ISRCTN16339434).

BACKGROUND: Alcohol misuse and emotional problems (ie, depression and anxiety) are highly comorbid among Canadian young adults. However, there is a lack of integrated, accessible, and evidence-based treatment options for these young adults.OBJECTIVE: The main goal of this study is to develop and test the efficacy of an integrated, online self-help program designed to target both alcohol misuse and emotional problems.METHODS: A two-arm randomized controlled trial design will be used to compare the efficacy of the online integrated treatment to a psychoeducational control group. A target sample of 214 participants will be recruited and randomly assigned to either condition. The integrated treatment will last 8 weeks, and participants will work through 12 modules. Modules will incorporate content based on principles of cognitive behavioral therapy and motivational interviewing. Participants in the control group will receive links to psychoeducational resources and will have access to the full treatment after follow-up. The primary outcome will be the number of Canadian standard drinks consumed in the week leading up the assessment. Secondary outcomes of interest include symptoms of depression, anxiety, alcohol-related problems, quality of life, and use of other drugs. Assessments will be completed at 3 time-points: at baseline, at the end of treatment (ie, 8 weeks), and at follow-up (ie, 24 weeks). Upon completion, data will be analyzed using generalized linear mixed models.RESULTS: Data collection began in June 2018 and will continue until January 2020. Final study results will be submitted for publication by July 2020.CONCLUSIONS: Currently, there are no integrated treatments designed to target alcohol misuse and the range of emotional problems experienced by young adults. This research stands to provide an effective, accessible (ie, Web-based), and feasible option to treat the many struggling young adults in this country.TRIAL REGISTRATION: ClinicalTrials.gov ID NCT03406039; https://clinicaltrials.gov/ct2/show/NCT03406039 (Archived by WebCite at http://www.webcitation.org/72fDefnrh).REGISTERED REPORT IDENTIFIER: PRR1-10.2196/11298.

Carpal tunnel syndrome (CTS) and spinal canal stenosis can be frequently observed in the medical history of patients with transthyretin amyloidosis (ATTR), both in the hereditary (mt-ATTR) and wild-type (wt-ATTR) form. The aim of this retrospective single-center analysis was to determine the prevalence of these findings, delay to diagnosis of systemic amyloidosis and the prognostic value in a large cohort of patients with wt-ATTR and mt-ATTR amyloidosis. Medical records of 253 patients diagnosed with wt-ATTR, 136 patients with mt-ATTR and 77 asymptomatic gene carriers were screened for history of CTS and spinal canal stenosis and laboratory analysis, electrocardiography and echocardiographic results, respectively. Clinical follow-up was performed by phone assessment. History of CTS was present in 77 patients (56%) with mt-ATTR, in 152 patients (60%) with wt-ATTR and even in 10 of the asymptomatic gene carriers (13%). Latency between carpal tunnel surgery and first diagnosis of systemic amyloidosis was significantly longer in wt-ATTR compared to mt-ATTR (117 ± 179 months vs. 66 ± 73 months; p = 0.02). In total, 36 patients (14%) with wt-ATTR and 7 patients (5%) with mt-ATTR had a history of clinically significant spinal canal stenosis. In the subgroup of mt-ATTR, patients with CTS had thicker IVS (19 ± 5 mm vs. 16 ± 5 mm, p

Background In recent years, cocaine use has increased in many countries, but only a minority of users seek treatment. Cognitive behavioral therapy (CBT) is seen as first-choice face-to-face treatment. However, a web-based intervention might serve as an alternative. Aims To test the efficacy of a web-based self-help intervention, with and without chat counseling, grounded in CBT, at reducing cocaine use in cocaine misusers not in treatment for a substance use disorder. Methods Subjects were randomly assigned to (1) a self-help intervention with chat support, (2) a self-help intervention without chat support, or (3) a waiting list control group. The fully-automated self-help program consisted of eight modules based on motivational interviewing, self-control practices and CBT. The primary outcome was the quantity of cocaine use per week. Secondary outcomes included frequency of cocaine and other substance use and mental health symptoms. Linear regression analysis was performed to investigate changes in primary and secondary outcomes. Results In total, 416 users registered online for the trial, of whom 311 completed the baseline assessment. Participants were predominantly male (73%) and averaged 33 years old (SD = 7.6). Despite considerable efforts on our part, only 47 of 311 (15.1%) subjects completed the 6-month follow-up assessment. Frequency of cocaine use and severity of cocaine dependence decreased only in the intervention groups. No significant difference in the primary outcome was observed between the study arms, but several differences in secondary outcomes were observed by complete case analyses. Conclusions Many cocaine misusers from the general population and not otherwise in treatment could be reached and decreased their cocaine use utilizing a CBT-based online intervention. However, due to the high percentage of dropouts and serious difficulties reaching subjects for follow-up assessments, no conclusions can be drawn regarding study arm differences. Implications for future studies are discussed., Highlights • Chat counseling in addition to web-based self-help in cocaine misusers from the general population was investigated in a RCT. • Many cocaine misusers were reached and reduced their use with a CBT-based online program with or without chat counseling. • Due to dropouts and serious difficulties reaching subjects for follow-ups, differences between study arms are inconclusive.

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding hidden effects in the data. In recent years, approaches based on interpreting a previously trained neural network classifier have become the de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we discuss a limitation of these approaches which may lead to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative Adversarial Networks (WGAN), which does not suffer from this limitation. We show that our proposed method performs substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging data from patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects., Accepted to CVPR 2018

Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model's performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers., EMBC 2018

Usability of medical devices is one of the main determining factors in preventing use errors in treatment and strongly correlates to patient safety and quality of treatment. This thesis demonstrates the usability testing and evaluation of a prototype for locomotor therapy of infants. Therefore, based on the normative requirements of the EN 62366, a concept combined of evaluation procedures and assessing methods was created to enable extensive testing and analysis of the different aspects of usability. On the basis of gathered information weak points were identified and appropriate measures were presented to increase the usability and operating safety of the locomotor prototype. The overall outcome showed an usability value of 77.4% and an evaluation score of 6.99, which can be interpreted as “satisfactory”.

The aim of this work was to analyse the medication process and the potential for errors during the process steps. For this purpose, a literature review was conducted and a questionnaire was developed to compare the results from the literature with the current medication process in Styrian hospitals. The questionnaire was divided into four different parts with a total of 29 questions. For the survey all 37 Styrian hospitals were contacted whereas 11 filled out the questionnaire. The survey showed that there is no standardized medication process defined and that the rate of medication errors is generally underestimated. In addition, technical solutions may help to reduce errors but are expected to be hardly used in clinical practice.

Background: In European countries, including Switzerland, cannabis is the most commonly used illicit drug. Offering a Web-based self-help tool could potentially reach users who otherwise would not seek traditional help. However, such Web-based self-help tools often suffer from low adherence. Objective: Through adherence-focused guidance enhancements, the aim of this study was to increase adherence in cannabis users entering a Web-based self-help tool to reduce their cannabis use and, in this way, augment its effectiveness. Methods: This paper presents the protocol for a three-arm randomized controlled trial (RCT) to compare the effectiveness of (1) an adherence-focused, guidance-enhanced, Web-based self-help intervention with social presence; (2) an adherence-focused, guidance-enhanced, Web-based self-help intervention without social presence; and (3) a treatment-as-usual at reducing cannabis use in problematic users. The two active interventions, each spanning 6 weeks, consist of modules designed to reduce cannabis use and attenuate common mental disorder (CMD) symptoms, including depression, anxiety, and stress-related disorder symptoms based on the approaches of motivational interviewing and cognitive behavioral therapy. With a target sample size of 528, data will be collected at baseline, 6 weeks, and 3 months after baseline. The primary outcome measurement will be the number of days of cannabis use on the preceding 7 days. Secondary outcomes will include the quantity of cannabis used in standardized cannabis joints, the severity of cannabis dependence, changes in CMD symptoms, and adherence to the program. Data analysis will follow the intention-to-treat principle and employ (generalized) linear mixed models. Results: The project commenced in August 2016; recruitment is anticipated to end by December 2018. First results are expected to be submitted for publication in summer 2019. Conclusions: This study will provide detailed insights on if and how the effectiveness of a Web-based self-help intervention aiming to reduce cannabis use in frequent cannabis users can be improved by theory-driven, adherence-focused guidance enhancement. Trial Registration: International Standard Randomized Controlled Trial Number Registry: ISRCTN11086185; http://www.isrctn.com/ISRCTN11086185 (Archived by WebCite at http://www.webcitation.org/6wspbuQ1M) [JMIR Res Protoc 2018;7(1):e30]

Accurate segmentation of the heart is an important step towards evaluating cardiac function. In this paper, we present a fully automated framework for segmentation of the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and 3D convolutional neural network architectures for this task. Experiments were performed on the ACDC 2017 challenge training dataset comprising cardiac MR images of 100 patients, where manual reference segmentations were made available for end-diastolic (ED) and end-systolic (ES) frames. We find that processing the images in a slice-by-slice fashion using 2D networks is beneficial due to a relatively large slice thickness. However, the exact network architecture only plays a minor role. We report mean Dice coefficients of 0.950 (LV), 0.893 (RV), and 0.899 (Myo), respectively with an average evaluation time of 1.1 s per volume on a modern GPU.

Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation algorithms are based on deep neural networks and rely on large datasets with full pixel-wise annotations. Producing such annotations can often only be done by medical professionals and requires large amounts of valuable time. Training a medical image segmentation network with weak annotations remains a relatively unexplored topic. In this work we investigate training strategies to learn the parameters of a pixel-wise segmentation network from scribble annotations alone. We evaluate the techniques on public cardiac (ACDC) and prostate (NCI-ISBI) segmentation datasets. We find that the networks trained on scribbles suffer from a remarkably small degradation in Dice of only 2.9% (cardiac) and 4.5% (prostate) with respect to a network trained on full annotations.

Convolutional neural networks (CNNs) have shown promising results on several segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs may degrade severely when segmenting images acquired with different scanners and/or protocols as compared to the training data, thus limiting their practical utility. We address this shortcoming in a lifelong multi-domain learning setting by treating images acquired with different scanners or protocols as samples from different, but related domains. Our solution is a single CNN with shared convolutional filters and domain-specific batch normalization layers, which can be tuned to new domains with only a few (\({\approx }\)4) labelled images. Importantly, this is achieved while retaining performance on the older domains whose training data may no longer be available. We evaluate the method for brain structure segmentation in MR images. Results demonstrate that the proposed method largely closes the gap to the benchmark, which is training a dedicated CNN for each scanner.

Accurate segmentation of medical images is an important step towards analyzing and tracking disease related morphological alterations in the anatomy. Convolutional neural networks (CNNs) have recently emerged as a powerful tool for many segmentation tasks in medical imaging. The performance of CNNs strongly depends on the size of the training data and combining data from different sources is an effective strategy for obtaining larger training datasets. However, this is often challenged by heterogeneous labeling of the datasets. For instance, one of the dataset may be missing labels or a number of labels may have been combined into a super label. In this work we propose a cost function which allows integration of multiple datasets with heterogeneous label subsets into a joint training. We evaluated the performance of this strategy on thigh MR and a cardiac MR datasets in which we artificially merged labels for half of the data. We found the proposed cost function substantially outperforms a naive masking approach, obtaining results very close to using the full annotations.

Automatically detecting acoustic shadows is of great importance for automatic 2D ultrasound analysis ranging from anatomy segmentation to landmark detection. However, variation in shape and similarity in intensity to other structures make shadow detection a very challenging task. In this paper, we propose an automatic shadow detection method to generate a pixel-wise, shadow-focused confidence map from weakly labelled, anatomically-focused images. Our method: (1) initializes potential shadow areas based on a classification task. (2) extends potential shadow areas using a GAN model. (3) adds intensity information to generate the final confidence map using a distance matrix. The proposed method accurately highlights the shadow areas in 2D ultrasound datasets comprising standard view planes as acquired during fetal screening. Moreover, the proposed method outperforms the state-of-the-art quantitatively and improves failure cases for automatic biometric measurement.

Background Myocardial T1 and extracellular volume (ECV) derived from cardiovascular MRIs are more and more widely accepted as important markers for diagnosis, risk prediction and monitoring of cardiac disease. Yet data regarding long-term stability of myocardial T1 mapping are lacking. The aim of this study was to investigate the long-term stability of native and postcontrast T1 mapping values in healthy volunteers. Methods 18 strictly selected healthy volunteers (52±10 years, 12 men) were studied on a Philips Achieva 1.5 Tesla scanner. T1 relaxation times were measured before and 15 min after a bolus contrast injection of gadolinium diethylenetriamine penta-acetic acid (DTPA) (0.2 mmol/kg) using a single-breath-hold modified Look-Locker inversion recovery 3(3)3(3)5 sequence. ECV was calculated using native and postcontrast T1 times of myocardium and blood correcting for blood haematocrit. Exams were repeated 3.6±0.5 years later under the same conditions and using the same scan protocols. Results Cardiac biomarkers (high-sensitivity troponin T and N terminal pro-brain natriuretic peptide) remained unchanged, as well as left ventricular mass, and global and longitudinal function. No significant change occurred regarding native T1 times (1017±24 ms vs 1015±21 ms; P=0.6), postcontrast T1 times (426±38 ms vs 413±20 ms; P=0.13) or ECV (22%±2% vs 23%±2%; P=0.3). Native T1 time and ECV appeared to be better reproducible than postcontrast T1, resulting in lower coefficients of variation (ECV: 3.5%, native T1: 1.3%, postcontrast T1: 6.4%) and smaller limits of agreement (ECV: 2%/−2%, native T1: 39 ms/−35 ms, postcontrast T1: 85 ms/−59 ms). Conclusions During long-term follow-up, native T1 and ECV values are very robust markers, whereas postcontrast T1 results appear less stable.

We introduce CompNN, a compositional method for the construction of a neural-network (NN) capturing the dynamic behavior of a complex analog multiple-input multiple-output (MIMO) system. CompNN first learns for each input/output pair (i, j), a small-sized nonlinear auto-regressive neural network with exogenous input (NARX) representing the transfer-function h ij . The training dataset is generated by varying input i of the MIMO, only. Then, for each output j, the transfer functions h ij are combined by a time-delayed neural network (TDNN) layer, f j . The training dataset for f j is generated by varying all MIMO inputs. The final output is f = (f 1 , ..., f n ). The NNs parameters are learned using Levenberg-Marquardt back-propagation algorithm. We apply CompNN to learn an NN abstraction of a CMOS band-gap voltage-reference circuit (BGR). First, we learn the NARX NNs corresponding to trimming, load-jump and line-jump responses of the circuit. Then, we recompose the outputs by training the second layer TDNN structure. We demonstrate the performance of our learned NN in the transient simulation of the BGR by reducing the simulation-time by a factor of 17 compared to the transistor-level simulations. CompNN allows us to map particular parts of the NN to specific behavioral features of the BGR. To the best of our knowledge, CompNN is the first method to learn the NN of an analog integrated circuit (MIMO system) in a compositional fashion.

This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.