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.

Mobile Health Technologies and Their Features Affecting Medication Adherence Among Cancer Patients: A Scoping Review.

This scoping review explores mobile health (mHealth) technologies and their features affecting medication adherence in cancer patients. Among 11 selected studies, predominantly from the USA, mHealth tools, particularly smartphone apps, were examined for their features in managing cancer patient's medication adherence. The studies highlighted the importance of adherence in continuous cancer therapy, with mHealth tools offering reminders and interactive features, that aim to enhance patient engagement.

Use of E-Consent in Healthcare Settings: A Scoping Review.

Electronic consent is a technology-driven approach that remains challenging in various healthcare settings. Transitioning from paper-based to electronic consent (e-consent) has streamlined the consent process. This scoping review explores patients' electronic consent in different healthcare settings.

Visualizing Mental Health Insights: A Pipeline from Social Media to Chernoff Faces.

This study proposes an approach for analyzing mental health through publicly available social media data, employing Large Language Models (LLMs) and visualization techniques to transform textual data into Chernoff Faces. The analysis began with a dataset comprising 15,744 posts sourced from major social media platforms, which was refined down to 2,621 posts through meticulous data cleaning, feature extraction, and visualization processes. Our methodology includes stages of Data Preparation, Feature Extraction, Chernoff Face Visualization, and Clinical Validation.

Unveiling the Potential of ChatGPT and YOLOv7 for Evaluating Children's Emotions Using Their Artistic Expressions.

Recent advancements in large language models (LLMs) have sparked considerable interest in their potential applications across various healthcare domains. One promising prospect is leveraging these generative models to accurately predict children's emotions by combining computer vision and natural language processing techniques. However, understanding children's emotional states based on their artistic expressions is equally crucial.

Social and Web Data Framework for Understanding Loneliness.

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness.

FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery.

FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. Utilizing a comprehensive dataset-the largest to date for fetal head metrics-FetSAM incorporates prompt-based learning. It distinguishes itself with a dual loss mechanism, combining Weighted DiceLoss and Weighted Lovasz Loss, optimized through AdamW and underscored by class weight adjustments for better segmentation balance.

Locating Loneliness Through Social Intelligence Analysis.

Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map.

Mapping loneliness through comparative analysis of USA and India using social intelligence analysis.

Loneliness, a widespread global public health concern, has far-reaching implications for mental and physical well-being, as well as economic productivity. It also increases the risk of life-threatening conditions. This study conducts a comparative analysis of loneliness in the USA and India using Twitter data, aiming to contribute to a global public health map on loneliness.

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.

Large-scale annotation dataset for fetal head biometry in ultrasound images.

This dataset features a collection of 3832 high-resolution ultrasound images, each with dimensions of 959×661 pixels, focused on Fetal heads. The images highlight specific anatomical regions: the brain, cavum septum pellucidum (CSP), and lateral ventricles (LV). The dataset was assembled under the Creative Commons Attribution 4.

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.

Understanding Loneliness in Younger People: Review of the Opportunities and Challenges for Loneliness Interventions.

Loneliness affects the quality of life of people all around the world. Loneliness is also shown to be directly associated with mental health issues and is often the cause of mental health problems. It is also shown to increase the risk of heart diseases and other physical illnesses.

Assessing the Landscape of Telehealth Interventions for Women's Health: An Umbrella Review of Recent Advances Post-Pandemic.

This umbrella review aims to provide a comprehensive overview of the use of telehealth services for women after the COVID-19 pandemic. The review synthesizes findings from 21 reviews, covering diverse topics such as cancer care, pregnancy and postpartum care, general health, and specific populations. While some areas have shown promising results, others require further research to better understand the potential of digital health interventions.

Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map.

Objectives: Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social intelligence analysis.

Settings And Design: This paper presents a proof of concept for the global loneliness map, using data collected in October 2022.

Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues.

Background: The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic.

Motivation: Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease.

The performance of serious games for enhancing attention in cognitively impaired older adults.

Attention, which is the process of noticing the surrounding environment and processing information, is one of the cognitive functions that deteriorate gradually as people grow older. Games that are used for other than entertainment, such as improving attention, are often referred to as serious games. This study examined the effectiveness of serious games on attention among elderly individuals suffering from cognitive impairment.

Understanding Correlations of Loneliness in India and USA.

Loneliness is a global public health issues contributing to a variety of mental and physical health issues. It also increases the risk of life-threatening conditions as well as contributes to burden on the economy in terms of the number of days lost to productivity. Loneliness is a highly varied concept though, which is a result of multiple factors.

Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techniques used for long-term prediction of T2DM and evaluate their performance, we conducted a scoping review using PRISMA-ScR.

Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review.

Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies.

The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review.

This scoping review explores the advantages and disadvantages of using ChatGPT in medical education. We searched PubMed, Google Scholar, Medline, Scopus, and Science Direct to identify relevant studies. Two reviewers independently conducted study selection and data extraction, followed by a narrative synthesis.

Scoping Review: Legal and Ethical Principles of Artificial Intelligence in Public Health.

The growing accessibility of large health datasets and AI's ability to analyze them offers significant potential to transform public health and epidemiology. AI-driven interventions in preventive, diagnostic, and therapeutic healthcare are becoming more prevalent, but they raise ethical concerns, particularly regarding patient safety and privacy. This study presents a thorough analysis of ethical and legal principles found in the literature on AI applications in public health.

Using AI for Detection, Prediction and Classification of Retinal Detachment.

The current state of machine learning (ML) and deep learning (DL) algorithms used to detect, classify and predict the onset of retinal detachment (RD) were examined in this scoping review. This severe eye condition can cause vision loss if left untreated. By analyzing the medical imaging modalities such as fundus photography, AI could help to detect peripheral detachment at an earlier stage.

Predicting Overall Survival in METABRIC Cohort Using Machine Learning.

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer that presents very high relapse and mortality. However, due to differences in the genetic architecture associated with TNBC, patients have different outcomes and respond differently to available treatments. In this study, we predicted the overall survival of TNBC patients in the METABRIC cohort employing supervised machine learning to identify important clinical and genetic features that are associated with better survival.

Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning.

The optical disc in the human retina can reveal important information about a person's health and well-being. We propose a deep learning-based approach to automatically identify the region in human retinal images that corresponds to the optical disc. We formulated the task as an image segmentation problem that leverages multiple public-domain datasets of human retinal fundus images.