Effectiveness of social robots as a tutoring and learning companion: a bibliometric analysis

AbstractA long-term perspective on how technology will mature is needed whereby robotics and artificial intelligence (AI) have accomplished a consequential and remarkable impact by finding their way into mainstream higher education. Robots have already become an indispensable factor in society and possess high potency as a part of educational technology. Social robot education is limited to complementing the digital aptitude of students in the world of information, and the role of social robots is crucial in polishing students ‘cognitive and social abilities. This study reviews the effectiveness of social robots in education, where we highlight the application of educational robots, surrounded by a blend of social robots and enactive didactics, which could lead to promising ideas for tutoring activities in education. It is empirically proven that social robots can assist with literature, science, or technology education. We synthesize the role of social robots in education and weigh their pros and cons by examining the impact of their appearance on robots’ performance as tutors, tools, or peers in learning exercises. The current study is the first bibliometric analysis that reflects robots’ impact in the education field as tutors and learning companions. A total of 288 articles were reviewed, and the data were extracted to construct an overview through bibliometrics. The outcome of this study paves the way for educational institutes to make informed and fruitful decisions on the applicability of robots, which can help them comprehend the learning styles of students and create knowledgeable and well-adjusted learners

Effectiveness of social robots as a tutoring and learning companion: a bibliometric analysis

A long-term perspective on how technology will mature is needed whereby robotics and artificial intelligence (AI) have accomplished a consequential and remarkable impact by finding their way into mainstream higher education. Robots have already become an indispensable factor in society and possess high potency as a part of educational technology. Social robot education is limited to complementing the digital aptitude of students in the world of information, and the role of social robots is crucial in polishing students ‘cognitive and social abilities. This study reviews the effectiveness of social robots in education, where we highlight the application of educational robots, surrounded by a blend of social robots and enactive didactics, which could lead to promising ideas for tutoring activities in education. It is empirically proven that social robots can assist with literature, science, or technology education. We synthesize the role of social robots in education and weigh their pros and cons by examining the impact of their appearance on robots’ performance as tutors, tools, or peers in learning exercises. The current study is the first bibliometric analysis that reflects robots’ impact in the education field as tutors and learning companions. A total of 288 articles were reviewed, and the data were extracted to construct an overview through bibliometrics. The outcome of this study paves the way for educational institutes to make informed and fruitful decisions on the applicability of robots, which can help them comprehend the learning styles of students and create knowledgeable and well-adjusted learners.</p

Face Masks and Respirators in the Fight Against the COVID-19 Pandemic: A Review of Current Materials, Advances and Future Perspectives

The outbreak of COVID-19 has spread rapidly across the globe, greatly affecting how humans as a whole interact, work and go about their daily life. One of the key pieces of personal protective equipment (PPE) that is being utilised to return to the norm is the face mask or respirator. In this review we aim to examine face masks and respirators, looking at the current materials in use and possible future innovations that will enhance their protection against SARS-CoV-2. Previous studies concluded that cotton, natural silk and chiffon could provide above 50% efficiency. In addition, it was found that cotton quilt with a highly tangled fibrous nature provides efficient filtration in the small particle size range. Novel designs by employing various filter materials such as nanofibres, silver nanoparticles, and nano-webs on the filter surfaces to induce antimicrobial properties are also discussed in detail. Modification of N95/N99 masks to provide additional filtration of air and to deactivate the pathogens using various technologies such as low- temperature plasma is reviewed. Legislative guidelines for selecting and wearing facial protection are also discussed. The feasibility of reusing these masks will be examined as well as a discussion on the modelling of mask use and the impact wearing them can have. The use of Artificial Intelligence (AI) models and its applications to minimise or prevent the spread of the virus using face masks and respirators is also addressed. It is concluded that a significant amount of research is required for the development of highly efficient, reusable, anti-viral and thermally regulated face masks and respirators

Demographics and Trends of Hypertrophic Cardiomyopathy-Related Mortality in the United States, 1999-2020

There are limited data on the mortality trends of HCM in the United States. To study the demographics and trends of mortality in patients with HCM, a retrospective cohort analysis was done with mortality data of patients with HCM listed as an underlying cause of death in the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database (CDC-WONDER) from January 1999 to December 2020. The analysis took place in February 2022. First, we measured HCM-related age-adjusted mortality rate (AAMR) per 100,000 US population stratified by sex, race, ethnicity, and geographic area. We then calculated the Annual Percentage Change (APC) for AAMR for each. A total of 24,655 HCM-related deaths occurred between 1999 and 2020. The AAMR for HCM-related deaths declined from 0.5/100,000 patients in 1999 to 0.2 in 2020. The APC changes are as follows: -6.8 (95% CI: -11.8 to -1.5) from 2002 to 2009, -1.23 (95% CI -13.8 to 13.2) from 2009 to 2014, -6.71 (95% CI -46.2 to 61.7) from 2014 to 2017 and remained at 2.07 (95% CI -26.1 to 41.1) from 2017 to 2020. Men had consistently higher AAMR than women. Overall, AAMR in men was 0.4 (95% CI: 0.4-0.5), and in women was 0.3 (95% CI: 0.3-0.3). A similar trend was noticed in men and women over the years, starting from 1999 (AAMR men: 0.7 and women: 0.4) to 2020 (AAMR men: 0.3 and women: 0.2). AAMRs were highest among black or African American patients 0.6 (95% CI: 0.5-0.6), followed by non-Hispanic and Hispanic white 0.3 (95% CI 0.3-0.3) and Asian or Pacific Islander 0.2 (95% CI 0.2-0.2). There was substantial variation in each region in the US. States such as California, Ohio, Michigan, Oregon, and Wyoming had the highest AAMR. Large metropolitan cities had higher AAMR than non-metropolitan cities. During the study period from 1999 to 2020, HCM-related mortality steadily decreased. The highest AAMR was observed among men, black patients, and residents of metropolitan areas. States such as California, Ohio, Michigan, Oregon, and Wyoming had the highest AAMR

CD44-SNA1 integrated cytopathology for delineation of high grade dysplastic and neoplastic oral lesions.

The high prevalence of oral potentially-malignant disorders exhibits diverse severity and risk of malignant transformation, which mandates a Point-of-Care diagnostic tool. Low patient compliance for biopsies underscores the need for minimally-invasive diagnosis. Oral cytology, an apt method, is not clinically applicable due to a lack of definitive diagnostic criteria and subjective interpretation. The primary objective of this study was to identify and evaluate the efficacy of biomarkers for cytology-based delineation of high-risk oral lesions. A comprehensive systematic review and meta-analysis of biomarkers recognized a panel of markers (n: 10) delineating dysplastic oral lesions. In this observational cross sectional study, immunohistochemical validation (n: 131) identified a four-marker panel, CD44, Cyclin D1, SNA-1, and MAA, with the best sensitivity (>75%; AUC>0.75) in delineating benign, hyperplasia, and mild-dysplasia (Low Risk Lesions; LRL) from moderate-severe dysplasia (High Grade Dysplasia: HGD) along with cancer. Independent validation by cytology (n: 133) showed that expression of SNA-1 and CD44 significantly delineate HGD and cancer with high sensitivity (>83%). Multiplex validation in another cohort (n: 138), integrated with a machine learning model incorporating clinical parameters, further improved the sensitivity and specificity (>88%). Additionally, image automation with SNA-1 profiled data set also provided a high sensitivity (sensitivity: 86%). In the present study, cytology with a two-marker panel, detecting aberrant glycosylation and a glycoprotein, provided efficient risk stratification of oral lesions. Our study indicated that use of a two-biomarker panel (CD44/SNA-1) integrated with clinical parameters or SNA-1 with automated image analysis (Sensitivity >85%) or multiplexed two-marker panel analysis (Sensitivity: >90%) provided efficient risk stratification of oral lesions, indicating the significance of biomarker-integrated cytopathology in the development of a Point-of-care assay

Mobile-based oral cancer classification for point-of-care screening.

SignificanceOral cancer is among the most common cancers globally, especially in low- and middle-income countries. Early detection is the most effective way to reduce the mortality rate. Deep learning-based cancer image classification models usually need to be hosted on a computing server. However, internet connection is unreliable for screening in low-resource settings.AimTo develop a mobile-based dual-mode image classification method and customized Android application for point-of-care oral cancer detection.ApproachThe dataset used in our study was captured among 5025 patients with our customized dual-modality mobile oral screening devices. We trained an efficient network MobileNet with focal loss and converted the model into TensorFlow Lite format. The finalized lite format model is ∼16.3  MB and ideal for smartphone platform operation. We have developed an Android smartphone application in an easy-to-use format that implements the mobile-based dual-modality image classification approach to distinguish oral potentially malignant and malignant images from normal/benign images.ResultsWe investigated the accuracy and running speed on a cost-effective smartphone computing platform. It takes ∼300  ms to process one image pair with the Moto G5 Android smartphone. We tested the proposed method on a standalone dataset and achieved 81% accuracy for distinguishing normal/benign lesions from clinically suspicious lesions, using a gold standard of clinical impression based on the review of images by oral specialists.ConclusionsOur study demonstrates the effectiveness of a mobile-based approach for oral cancer screening in low-resource settings

Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images

SignificanceOral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.AimWe aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.ApproachThis work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.ResultsThe proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.ConclusionsOur study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved