Hybrid Deep Learning and Machine Learning for Detecting Hepatocyte Ballooning in Liver Ultrasound Images.

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options.

Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images.

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.

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.

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 SiO to NaO: 1.

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.

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.

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.

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.

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).

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.

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 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.

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.

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.

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).

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.

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.

[Barriers and Resources in Inpatient Psychosomatic Rehabilitative Care of Migrant Patients].

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.

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.

Perioperative Changes of Right Ventricular Function in Cardiac Surgical Patients Assessed by Myocardial Deformation Analysis and 3-Dimensional Echocardiography.

Objectives: To test the hypothesis that longitudinal strain of the right ventricle (RV) is significantly reduced in patients undergoing cardiac surgery with extracorporeal circulation and cardioplegic cardiac arrest at the end of surgery, whereas RV ejection fraction remains unchanged.

Design: Prospective observational cohort study.

Setting: Single university hospital.

An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e.

Developing a Digital Mental Health Platform for the Arab World: From Research to Action.

Individuals within the Arab world rarely access mental health services. One of the major reasons for this relates to the stigma associated with mental disorders. According to the World Health Organization (WHO), untreated and undiagnosed individuals living with moderate to severe mental health disorders are more likely to die 10-20 years earlier than the estimated life expectancy of the general population.