Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.

BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest. OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study. METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance). RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: "virus of COVID-19," "risk factors of COVID-19," "prevention of COVID-19," "treatment of COVID-19," "health care delivery during COVID-19," "and impact of COVID-19." The most prominent topic, observed in over half of the analyzed studies, was "the impact of COVID-19." CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Remote operation of cross-sectional imaging devices as a new form of teleoperation: Structural, technical, regulatory, and qualification aspects in Germany.

BACKGROUND: There is a significant shortage of radiographers in Germany and this shortage is expected to increase. Thus, it is becoming increasingly difficult for radiological facilities to adequately provide their services for the required period of time. Teleradiology has already been established for electronic transmission of diagnostic radiographic imaging examinations between two geographical locations for diagnostic reporting. Recently, the concept of teleoperating radiological devices has become increasingly attractive. METHOD: We examined the potential of teleoperating magnetic resonance imaging (MRI) in radiological facilities within the German regulatory framework in order to address the shortage of qualified personnel. To this end, we are introducing the concept of remote scanning, the structural foundations, the technical requirements associated with it, as well as the legal and educational qualifications of the relevant professional groups. Furthermore, suggestions regarding nomenclature and necessary standard operating procedures to efficiently integrate teleoperation into a clinical workflow adhering to high patient safety standards are provided. RESULTS: Companies provide technical solutions or even experienced radiographers as a service on demand for teleoperating radiological imaging devices remotely from a distance. There should be a comprehensive on-site strategy to effectively embed remote scanning into clinics. Local information technology and data security institutions should be involved in implementation. In order to guarantee that the remote operation workflow is able to provide health care services in line with regulative and legal standards, it is essential to implement standardized personal and institutional training, certifications, and accreditation procedures. Standard operating procedures (SOPs) and checklists for the involved staff, which are adapted to the local workflow in the participating facilities, are beneficial. CONCLUSION: Remote MRI scanning is an evolving technology that further expands the concept of teleradiology to include teleoperations and provides flexibility with respect to the staffing of MRI operators. Careful consideration and dedicated expertise of all involved parties are required to ensure patient safety, meet regulations, and successfully integrate teleoperations into clinics. KEY POINTS: · Remote MRI scanning expands the concept of teleradiology.. · Remote scanning provides flexibility regarding the staffing of MRI operators.. · IT and data security institutions should be involved when implementing remote scanning.. · Comprehensible SOPs and checklists should be established for remote MRI scanning.. · Radiation protection legislation does not allow purely remote CT scanning..

Effect of Microwaves on the Rapid Curing of Metakaolin- and Aluminum Orthophosphate-Based Geopolymers.

This paper deals with the influence of microwaves on the hardening and curing of geopolymer binders synthesized from metakaolin or aluminum orthophosphate with sodium silicate solution as the activator. Pure geopolymer pastes as well as geopolymer mortars were considered. The variable parameters were the modulus of the sodium silicate solutions (molar ratio of SiO2 to Na2O: 1.5, 2.0 and 2.5) and the Si/Al ratio (3/1 and 2/1). Selected samples were cured in a microwave oven until hardening, so the curing time depended on the mixture. For comparison some samples were cured at ambient temperature. To investigate the influence of microwave radiation on the reaction kinetics, isothermal heat flow calorimetry, ultrasonic velocity measurements and rheological investigations into the variation of curing temperature were used. In addition, the mechanical properties of the cured samples were characterized. The results show that microwave curing only takes a few minutes, so it is the most time-saving method. Key factors influencing the geopolymer reaction under microwave radiation are the raw materials as well as the Si/Al ratio. Metakaolin-based geopolymer binders are more stable than those based on aluminum orthophosphate, especially regarding their salt efflorescence. Microwave radiation is an efficient method to accelerate the geopolymer reaction.

The metaverse digital environments: a scoping review of the challenges, privacy and security issues.

The concept of the "metaverse" has garnered significant attention recently, positioned as the "next frontier" of the internet. This emerging digital realm carries substantial economic and financial implications for both IT and non-IT industries. However, the integration and evolution of these virtual universes bring forth a multitude of intricate issues and quandaries that demand resolution. Within this research endeavor, our objective was to delve into and appraise the array of challenges, privacy concerns, and security issues that have come to light during the development of metaverse virtual environments in the wake of the COVID-19 pandemic. Through a meticulous review and analysis of literature spanning from January 2020 to December 2022, we have meticulously identified and scrutinized 29 distinct challenges, along with 12 policy, privacy, and security matters intertwined with the metaverse. Among the challenges we unearthed, the foremost were concerns pertaining to the costs associated with hardware and software, implementation complexities, digital disparities, and the ethical and moral quandaries surrounding socio-control, collectively cited by 43%, 40%, and 33% of the surveyed articles, respectively. Turning our focus to policy, privacy, and security issues, the top three concerns that emerged from our investigation encompassed the formulation of metaverse rules and principles, the encroachment of privacy threats within the metaverse, and the looming challenges concerning data management, all mentioned in 43%, 40%, and 33% of the examined literature. In summation, the development of virtual environments within the metaverse is a multifaceted and dynamically evolving domain, offering both opportunities and hurdles for researchers and practitioners alike. It is our aspiration that the insights, challenges, and recommendations articulated in this report will catalyze extensive dialogues among industry stakeholders, governmental bodies, and other interested parties concerning the metaverse's destiny and the world they aim to construct or bequeath to future generations.

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.

Efficacy of ibuprofen and indomethacin as prophylaxis of heterotopic ossification: a comparative study.

The prophylactic action of non-steroidal anti-inflammatory drugs (NSAIDs) in heterotopic ossification (HO) was first described following analgesic therapy with indomethacin. Following that evidence, several compounds have been successfully used for prophylaxes of HO. Ibuprofen has been also proposed for the prevention of HO following THA. The present study compared the administration of ibuprofen for three weeks versus indomethacin as prophylaxis for HO following primary THA. In all THA procedures, pre- and post-operative protocols were conducted in a highly standardized fashion. The type of HO prophylaxis (indomethacin 100 mg/daily or ibuprofen 100 mg/daily) was chosen according to a chronological criterion: from 2017 to 2019 indomethacin was used, whereas from 2019 to 2022 ibuprofen was administered. In case of allergy or intolerance to NSAIDs, no prophylaxis was performed, and patients were included as a control group. All patients who underwent an anteroposterior radiography of the pelvis at a minimum of 12 months following THA were considered for inclusion. On admission, the age and sex of the patients were recorded. Moreover, the causes of osteoarthritis and the date of surgery were recorded. The grade of HO was assigned by a blinded assessor who was not involved in the clinical management of the patients. The modified Brooker Staging System was used to rate the efficacy of the interventions. Data from 1248 patients were collected. 62% (767 of 1248 patients) were women. The mean age was 67.0 ± 2.9 years. The mean follow-up was 21.1 ± 10.8 months. In the ibuprofen group, 73% of patients evidenced Brooker 0, 17% Brooker I, and 10% Brooker II. In the indomethacin group, 77% of patients evidenced Brooker 0, 16% Brooker I, 6% Brooker II. No patient in the ibuprofen and indomethacin group developed Brooker III or IV. In the control group, 64% of patients evidenced Brooker 0, 21% Brooker I, 3% Brooker II, and 12% Brooker III. No patient in the control group developed Brooker IV HO. Concluding, three weeks of administration of ibuprofen demonstrated similar efficacy to indomethacin in preventing HO following primary THA. The prophylaxis with ibuprofen or indomethacin was more effective in preventing HO compared to a control group who did not receive any pharmacological prophylaxis.

Robotic-assisted total knee arthroplasty in clinical practice: protocol for a randomised controlled trial.

Between 2 and 20% of patients who undergo total knee arthroplasty (TKA) report restricted motion and anterior knee pain. Non-optimal alignment of the implant components is a common cause of such complaints. Robotic-assisted TKA has been advocated to improve the accuracy of component positioning to match patients' anatomy and biomechanics. However, the advantages of robotic surgery over conventional freehand TKA are still unclear. The present study is a protocol for a single-blind clinical trial in which patients will be randomly allocated to undergo either robotic-assisted TKA or conventional freehand TKA. A restricted kinematic alignment with medial para-stellar approach shall be made in all patients. The present study follows the SPIRIT guidelines. The primary outcome of interest is to compare robotic TKA versus traditional freehand TKA in terms of patient-reported outcome measures (PROMs), length of hospitalisation, blood values, blood transfusion units, and range of motion. The second outcome of interest is to evaluate the accuracy of component positioning of robotic-assisted TKA compared to the conventional freehand TKA.Level of evidence Level I, randomised controlled trial.Registration German Registry of Clinical Trials (ID: DRKS00030614).

Investigations on Stability of Polycarboxylate Superplasticizers in Alkaline Activators for Geopolymer Binders.

Calcined clays are interesting starting materials to be used as SCMs (supplementary cementitious materials) in cements or to be converted to geopolymers by activation with a high alkaline activator. The adjustment of the properties in the fresh state, especially regarding the consistency of these binders, is almost exclusively achieved by the addition of water, since commercially available superplasticizers seem to be ineffective in low-calcium geopolymer systems. The aim of this study was a systematic investigation of various PCE (polycarboxylate ester/ether) superplasticizers (methacrylate ester PCE: MPEG, isoprenol ether PCE: IPEG, methallyl ether PCE: HPEG) with respect to their stability in different alkaline activators (NaOH, KOH, sodium and potassium silicate solutions). The effectiveness of superplasticizers (SPs) in low-calcium geopolymer binders was verified by rheological tests. Size exclusion chromatography was used to investigate if structural degradation of the superplasticizers occurs. The investigated PCE superplasticizers showed a thickening effect in the low-calcium geopolymer system. Depending on the alkalinity of the activator solution, a degradation process was detected for all the PCEs investigated. The side chains of the PCEs are cleaved off the backbone by basic ester and ether hydrolysis. The highest degree of degradation was found in sodium and potassium silicate solutions. In alkaline hydroxide solutions, the degradation process increases with increasing alkalinity.

Wearable devices for anxiety & depression: A scoping review.

Background: The rates of mental health disorders such as anxiety and depression are at an all-time high especially since the onset of COVID-19, and the need for readily available digital health care solutions has never been greater. Wearable devices have increasingly incorporated sensors that were previously reserved for hospital settings. The availability of wearable device features that address anxiety and depression is still in its infancy, but consumers will soon have the potential to self-monitor moods and behaviors using everyday commercially-available devices. Objective: This study aims to explore the features of wearable devices that can be used for monitoring anxiety and depression. Methods: Six bibliographic databases, including MEDLINE, EMBASE, PsycINFO, IEEE Xplore, ACM Digital Library, and Google Scholar were used as search engines for this review. Two independent reviewers performed study selection and data extraction, while two other reviewers justified the cross-checking of extracted data. A narrative approach for synthesizing the data was utilized. Results: From 2408 initial results, 58 studies were assessed and highlighted according to our inclusion criteria. Wrist-worn devices were identified in the bulk of our studies (n = 42 or 71%). For the identification of anxiety and depression, we reported 26 methods for assessing mood, with the State-Trait Anxiety Inventory being the joint most common along with the Diagnostic and Statistical Manual of Mental Disorders (n = 8 or 14%). Finally, n = 26 or 46% of studies highlighted the smartphone as a wearable device host device. Conclusion: The emergence of affordable, consumer-grade biosensors offers the potential for new approaches to support mental health therapies for illnesses such as anxiety and depression. We believe that purposefully-designed wearable devices that combine the expertise of technologists and clinical experts can play a key role in self-care monitoring and diagnosis.

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network.

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review.

Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. We included 15 systematic reviews of 852 citations identified. The included reviews assessed the performance of AI models in diagnosing Alzheimer's disease (n = 7), mild cognitive impairment (n = 6), schizophrenia (n = 3), bipolar disease (n = 2), autism spectrum disorder (n = 1), obsessive-compulsive disorder (n = 1), post-traumatic stress disorder (n = 1), and psychotic disorders (n = 1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. Healthcare professionals in the field should cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety.

The Effectiveness of Serious Games in Alleviating Anxiety: Systematic Review and Meta-analysis.

BACKGROUND: Anxiety is a mental disorder characterized by apprehension, tension, uneasiness, and other related behavioral disturbances. One of the nonpharmacological treatments used for reducing anxiety is serious games, which are games that have a purpose other than entertainment. The effectiveness of serious games in alleviating anxiety has been investigated by several systematic reviews; however, they were limited by design and methodological weaknesses. OBJECTIVE: This study aims to assess the effectiveness of serious games in alleviating anxiety by summarizing the results of previous studies and providing an up-to-date review. METHODS: We conducted a systematic review of randomized controlled trials (RCTs). The following seven databases were searched: MEDLINE, CINAHL, PsycINFO, ACM Digital Library, IEEE Xplore, Scopus, and Google Scholar. We also conducted backward and forward reference list checking for the included studies and relevant reviews. Two reviewers independently carried out the study selection, data extraction, risk of bias assessment, and quality of evidence appraisal. We used a narrative and statistical approach, as appropriate, to synthesize the results of the included studies. RESULTS: Of the 935 citations retrieved, 33 studies were included in this review. Of these, 22 RCTs were eventually included in the meta-analysis. Very low-quality evidence from 9 RCTs and 5 RCTs showed no statistically significant effect of exergames (games entailing physical exercises) on anxiety levels when compared with conventional exercises (P=.70) and no intervention (P=.27), respectively. Although 6 RCTs demonstrated a statistically and clinically significant effect of computerized cognitive behavioral therapy games on anxiety levels when compared with no intervention (P=.01), the quality of the evidence reported was low. Similarly, low-quality evidence from 3 RCTs showed a statistically and clinically significant effect of biofeedback games on anxiety levels when compared with conventional video games (P=.03). CONCLUSIONS: This review shows that exergames can be as effective as conventional exercises in alleviating anxiety; computerized cognitive behavioral therapy games and exergames can be more effective than no intervention, and biofeedback games can be more effective than conventional video games. However, our findings remain inconclusive, mainly because there was a high risk of bias in the individual studies included, the quality of meta-analyzed evidence was low, few studies were included in some meta-analyses, patients without anxiety were recruited in most studies, and purpose-shifted serious games were used in most studies. Therefore, serious games should be considered complementary to existing interventions. Researchers should use serious games that are designed specifically to alleviate depression, deliver other therapeutic modalities, and recruit a diverse population of patients with anxiety.

Artificial Intelligence for Skin Cancer Detection: Scoping Review.

BACKGROUND: Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning-based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. OBJECTIVE: The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. METHODS: We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. RESULTS: We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. CONCLUSIONS: This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

Navigating with Augmented Reality - How does it affect drivers' mental load?

Drivers have been proven to easily understand Augmented Reality (AR) information. Especially in an ambiguous navigation task, drivers are expected to benefit from AR information. The driving simulator study was aimed at examining differences in mental load while navigating in an urban area with ambiguous intersection situations (N = 59). The navigation information was presented to the driver through a head-up display (HUD): a conventional HUD or an AR display, which relates information to the surroundings. Additionally, the driver had to solve a non-driving-related task (NDRT) which was an auditory cognitive, spatial task. Results showed that while driving with the AR display, participants performed better in the NDRT, which indicates a reduced mental load compared with the HUD. Participants drove on average 3 km/h slower with the HUD, showing compensation behaviour.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis.

BACKGROUND: Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. OBJECTIVE: We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. METHODS: We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most relevant COVID-19-related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub. RESULTS: Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19-related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread. CONCLUSIONS: We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

[Barriers and Resources in Inpatient Psychosomatic Rehabilitative Care of Migrant Patients]. / Barrieren und Ressourcen in der Behandlung von Patient/innen mit Migrationshintergrund in der psychosomatischen Rehabilitation.

Patients from migrant descent access inpatient psychosomatic rehabilitative care less and achieve less treatment success than patients from the host populations. They are confronted with different process barriers in the healthcare system which combined with individual barriers can inhibit successful treatment. Studies have shown that working with migrant patients may also be challenging for healthcare providers.This study aims to assess and compare barriers and resources faced by migrant and non-migrant patients during their treatment in inpatient psychosomatic rehabilitative care. Also, the aim is to assess and compare barriers and resources faced by healthcare providers in treating migrant and non-migrant patients in order to identify barriers and resources specific to working with migrant patients.A total of 77 semi-structured interviews were conducted (20 migrant and 19 non-migrant patients as well as 14 migrant and 24 non-migrant healthcare providers). Data were transcribed and analyzed applying the method of qualitative content analysis (Mayring) with inductive categories.Migrant and non-migrant patients stated that they profit from the treatment in inpatient psychosomatic rehabilitative care. The greatest barriers for both migrant patients and healthcare providers are language barriers, cultural differences, differences in expectations regarding the treatment and limited organizational cultural competences. As far as organizational cultural competences are implemented, they are profitable for migrant patients and non-migrant healthcare providers. However, migrant healthcare workers seem responsible for implementing culturally competent care and suffer from increased workload.

The Mixture Graph-A Data Structure for Compressing, Rendering, and Querying Segmentation Histograms.

In this paper, we present a novel data structure, called the Mixture Graph. This data structure allows us to compress, render, and query segmentation histograms. Such histograms arise when building a mipmap of a volume containing segmentation IDs. Each voxel in the histogram mipmap contains a convex combination (mixture) of segmentation IDs. Each mixture represents the distribution of IDs in the respective voxel's children. Our method factorizes these mixtures into a series of linear interpolations between exactly two segmentation IDs. The result is represented as a directed acyclic graph (DAG) whose nodes are topologically ordered. Pruning replicate nodes in the tree followed by compression allows us to store the resulting data structure efficiently. During rendering, transfer functions are propagated from sources (leafs) through the DAG to allow for efficient, pre-filtered rendering at interactive frame rates. Assembly of histogram contributions across the footprint of a given volume allows us to efficiently query partial histograms, achieving up to 178 x speed-up over naive parallelized range queries. Additionally, we apply the Mixture Graph to compute correctly pre-filtered volume lighting and to interactively explore segments based on shape, geometry, and orientation using multi-dimensional transfer functions.

Artificial Intelligence in the Fight Against COVID-19: Scoping Review.

BACKGROUND: In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. OBJECTIVE: This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. METHODS: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. RESULTS: We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. CONCLUSIONS: The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.