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. To address this challenge, this paper presents a pipelined architecture comprising YOLOv7 and the powerful GPT-3.5 Turbo language model, where YOLOv7 is employed for object detection using art therapy imaging annotations, while GPT-3.5 interprets the sketches. After rigorously evaluating the proposed framework through a series of comprehensive experiments, we observed that our model achieved high confidence scores for both object detection and emotion interpretation. The robust performance of the proposed framework not only aids in explaining children's art but also provides valuable insights for parents and therapists. This capability enables them to better understand children's emotional states based on their artistic expressions, ultimately facilitating improved support and care.

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. Dimensionality reduction techniques such as PCA, t-SNE, and UMAP were employed to transform complex mental health data into comprehensible visual representations. Validation involved a survey among 60 volunteer psychiatrists, underscoring the visualizations' potential for enhancing clinical assessments. This work sets the stage for future evaluations, specifically focusing on a combined features method to further refine the visual representation of mental health conditions and to augment the diagnostic tools available to mental health professionals.

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. We searched four databases and selected 14 studies that met our inclusion criteria. Our results show that E-consent is associated with key measures such as sufficient information, accuracy, enhanced shared decision-making, and efficiency. The majority of studies used a comparative design model to contrast paper-based consent with E-consent. Our findings provide an overview of the current state of E-consent use in healthcare settings.

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

Goal: FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. Methods: 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. Performance benchmarks against prominent models such as U-Net, DeepLabV3, and Segformer highlight its efficacy. Results: FetSAM delivers unparalleled segmentation accuracy, demonstrated by a DSC of 0.90117, HD of 1.86484, and ASD of 0.46645. Conclusion: FetSAM sets a new benchmark in AI-enhanced prenatal ultrasound analysis, providing a robust, precise tool for clinical applications and pushing the envelope of prenatal care with its groundbreaking dataset and segmentation capabilities.

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 4.0 International license, using previously anonymized and de-identified images to maintain ethical standards. Each image is complemented by a CSV file detailing pixel size in millimeters (mm). For enhanced compatibility and usability, the dataset is available in 11 universally accepted formats, including Cityscapes, YOLO, CVAT, Datumaro, COCO, TFRecord, PASCAL, LabelMe, Segmentation mask, OpenImage, and ICDAR. This broad range of formats ensures adaptability for various computer vision tasks, such as classification, segmentation, and object detection. It is also compatible with multiple medical imaging software and deep learning frameworks. The reliability of the annotations is verified through a two-step validation process involving a Senior Attending Physician and a Radiologic Technologist. The Intraclass Correlation Coefficients (ICC) and Jaccard similarity indices (JS) are utilized to quantify inter-rater agreement. The dataset exhibits high annotation reliability, with ICC values averaging at 0.859 and 0.889, and JS values at 0.855 and 0.857 in two iterative rounds of annotation. This dataset is designed to be an invaluable resource for ongoing and future research projects in medical imaging and computer vision. It is particularly suited for applications in prenatal diagnostics, clinical diagnosis, and computer-assisted interventions. Its detailed annotations, broad compatibility, and ethical compliance make it a highly reusable and adaptable tool for the development of algorithms aimed at improving maternal and Fetal health.

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.

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. The review identifies gaps in knowledge and highlights the need for more rigorous and comprehensive research to address the limitations and gaps identified in the current evidence base. This includes prioritizing the use of standardized guidelines, quality assessment tools, and meta-analyses, as well as exploring the comparative effectiveness of different digital health interventions, the experiences of specific populations, and the cost-effectiveness of these technologies. By addressing these gaps, this umbrella review can inform future research and policy decisions, ultimately improving women's health outcomes in the post-pandemic era.

Social Media Use and Depression in Adolescents: A Scoping Review.

This scoping review aimed to investigate the association between depression and social media use among adolescents. The study analyzed 43 papers using five databases to identify articles published from 2012 to August 2022. The results revealed a connection between social media use and depression, as well as other negative outcomes such as anxiety, poor sleep, low self-esteem, and social and appearance anxiety. Surveys were the most used study strategy, with multiple common scales applied to assess depression, social media use, and other factors such as self-esteem and sleep quality. Among the studies, eight reported that females who use social media showed higher depression symptoms than males. This scoping review provides an overview of the current literature on the relationship between social media use and depression among adolescents. The findings emphasize the importance of monitoring social media use and providing support for individuals struggling with depression. However, more research is needed to better understand the factors contributing to this relationship and to develop more standardized assessment methods.

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. A comprehensive search yielded 22 publications for review, revealing ethical principles such as equity, bias, privacy, security, safety, transparency, confidentiality, accountability, social justice, and autonomy. Additionally, five key ethical challenges were identified. The study emphasizes the importance of addressing these ethical and legal concerns and encourages further research to establish comprehensive guidelines for responsible AI implementation in public health.

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. Out of 197 references, 25 studies met the eligibility criteria. The primary applications of ChatGPT in medical education include automated scoring, teaching assistance, personalized learning, research assistance, quick access to information, generating case scenarios and exam questions, content creation for learning facilitation, and language translation. We also discuss the challenges and limitations of using ChatGPT in medical education, such as its inability to reason beyond existing knowledge, generation of incorrect information, bias, potential undermining of students' critical thinking skills, and ethical concerns. These concerns include using ChatGPT for exam and assignment cheating by students and researchers, as well as issues related to patients' privacy.

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. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.

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. Of the 40 papers included in this review, 23 studies used Machine Learning (ML) as the most common AI technique, with Deep Learning (DL) models used exclusively in four studies. Of the 13 studies that used both ML and DL, 8 studies employed ensemble learning models, and SVM and RF were the most used individual classifiers. Our findings highlight the importance of accuracy and recall as validation metrics, with accuracy being used in 31 studies, followed by recall in 29 studies. These discoveries emphasize the critical role of high predictive accuracy and sensitivity in detecting positive T2DM cases.

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. To understand loneliness this paper carries out a comparative analysis of USA and India through Twitter data on the keywords associated with loneliness. The comparative analysis on loneliness is in the vein of comparative public health literature and to contribute to develop a global public health map on loneliness. The results showed that the dynamics of loneliness through the topics correlated vary across geographical locations. Social media data can be used to capture the dynamics of loneliness which can vary from one place to another depending on the socioeconomic and cultural norms and sociopolitical policies.

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.

Chatbot features for anxiety and depression: A scoping review.

Chatbots can provide valuable support to patients in assessing and guiding management of various health problems particularly when human resources are scarce. Chatbots can be affordable and efficient on-demand virtual assistants for mental health conditions, including anxiety and depression. We review features of chatbots available for anxiety or depression. Six bibliographic databases were searched including backward and forwards reference list checking. The initial search returned 1302 citations. Post-filtering, 42 studies remained forming the final dataset for this scoping review. Most of the studies were from conference proceedings (62%, 26/42), followed by journal articles (26%, 11/42), reports (7%, 3/42), or book chapters (5%, 2/42). About half of the reviewed chatbots had functionality targeting both anxiety and depression (60%, 25/42), whereas 38% (16/42) targeted only depression, 38% (16/42) anxiety and the remaining addressed other mental health issues along with anxiety and depression. Avatars or fictional characters were rarely used in these studies only 26% (11/42) despite their increasing popularity. Mental health chatbots could benefit in helping patients with anxiety and depression and provide valuable support to mental healthcare workers, particularly when resources are scarce. Real-time personal virtual assistance fills in this gap. Their role in mental health care is expected to increase.

Machine learning models to detect anxiety and depression through social media: A scoping review.

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019-2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.

The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review.

Background: Brain traumas, mental disorders, and vocal abuse can result in permanent or temporary speech impairment, significantly impairing one's quality of life and occasionally resulting in social isolation. Brain-computer interfaces (BCI) can support people who have issues with their speech or who have been paralyzed to communicate with their surroundings via brain signals. Therefore, EEG signal-based BCI has received significant attention in the last two decades for multiple reasons: (i) clinical research has capitulated detailed knowledge of EEG signals, (ii) inexpensive EEG devices, and (iii) its application in medical and social fields. Objective: This study explores the existing literature and summarizes EEG data acquisition, feature extraction, and artificial intelligence (AI) techniques for decoding speech from brain signals. Method: We followed the PRISMA-ScR guidelines to conduct this scoping review. We searched six electronic databases: PubMed, IEEE Xplore, the ACM Digital Library, Scopus, arXiv, and Google Scholar. We carefully selected search terms based on target intervention (i.e., imagined speech and AI) and target data (EEG signals), and some of the search terms were derived from previous reviews. The study selection process was carried out in three phases: study identification, study selection, and data extraction. Two reviewers independently carried out study selection and data extraction. A narrative approach was adopted to synthesize the extracted data. Results: A total of 263 studies were evaluated; however, 34 met the eligibility criteria for inclusion in this review. We found 64-electrode EEG signal devices to be the most widely used in the included studies. The most common signal normalization and feature extractions in the included studies were the bandpass filter and wavelet-based feature extraction. We categorized the studies based on AI techniques, such as machine learning and deep learning. The most prominent ML algorithm was a support vector machine, and the DL algorithm was a convolutional neural network. Conclusions: EEG signal-based BCI is a viable technology that can enable people with severe or temporal voice impairment to communicate to the world directly from their brain. However, the development of BCI technology is still in its infancy.

Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction.

Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW.

A Conceptual Framework for Fetus Head Analysis Based on Ultrasound Images.

Ultrasound images are the most used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. In particular, the obstetrician uses fetus head images to monitor the growth state and identify essential features such as Gestational age (GA), estimated fetus weight (EFW), and brain anatomical structures. However, this work requires an expert obstetrician, and it is time-consuming and costly. Therefore, we proposed an automatic framework by adopting a hybrid approach that combines three components i) automatic segmentation to segment the region of interest (ROI) in the fetus head, ii) measurement extraction to measure the segmented ROI, and iii) anomaly and features detection to predict fetus GA, EFW, and abnormality status.

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.