RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier.
CONCLUSION: A deep-learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry.
Aims: This study investigated the impact of an extramural program involving PWD on dental students' professionalism and students' perception of training in managing patients with special needs.
Materials and Methods: A group of 165 undergraduate dental students (year 1 to year 5) participated in a voluntary program, involving 124 visually impaired children, at a special education school in Kuala Lumpur, Malaysia. A dedicated module in oral health was developed by specialists in special care dentistry, pedodontics, and medical sciences. Dental students then participated in a semi-structured focus group interview survey to discuss perceptions of their learning experiences. Qualitative data were analyzed via thematic analysis.
Results: The program had positive impact on various aspects categorized into four major domains: professional knowledge (e.g., understanding of oral-systemic-social-environmental health interaction and understanding of disability), professional skills (e.g., communication and organizational skills), professional behavior (e.g., empathy and teamwork), and value-added learning (e.g., photography and information technology skills). Students showed improved willingness to manage, and comfort in managing PWD, and expressed support for future educational programs involving this patient cohort.
Conclusion: Improved knowledge, skills, attitudes, and personal values, as well as support for future programs, indicate the positive impact of extramural educational activities involving PWD in developing professionalism in patient care, while providing an opportunity for students to be exposed to managing patients with special needs.
METHODS: All fourth-year undergraduate dental students (n = 69, response rate = 100%) participated in the Photodentistry learning activity developed by specialists from the areas of dentistry, arts, education, and psychology. A survey using the Toronto Empathy Questionnaire (TEQ) was conducted both pretest and posttest, followed by an open-ended written survey of their reflection towards the learning activity. Quantitative data were analyzed via paired t-test (P < 0.05), while qualitative data were analyzed using thematic analysis.
RESULTS: There was a significant increase in both students' total mean empathy score and the individual scores for 8 (out of 16) items of the TEQ after the learning activity. Students stated that they had an improved understanding of managing patients in a comprehensive manner (e.g., managing medically compromised patients, performing treatment planning, communication with patients who have special health care needs). Students also reported the development of skills (e.g., observation, critical thinking) and positive attitudes (e.g., empathy, responsibility) towards patients.
CONCLUSION: Photodentistry is an effective learning approach for improving dental students' empathy and learning experience in comprehensive patient care.
METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.
Objective: To analyze the video sources, contents and quality of YouTube videos about the topic of medical professionalism.
Methods: A systematic search was accomplished on YouTube videos during the period between March 1, 2020 and March 27, 2020. The phrases as significant words used throughout YouTube web search were 'Professionalism in Medical Education', Professionalism in medicine', 'Professionalism of medical students', 'Professionalism in healthcare'. 'Teaching professionalism', 'Attributes of professionalism'. The basic information collected for each video included author's/publisher's name, total number of watchers, likes, dislikes and positive and undesirable remarks. The videos were categorized into educationally useful and useless established on the content, correctness of the knowledge and the advices. Different variables were measured and correlated for the data analysis.YouTube website was searched the using keywords 'Professionalism in Medical Education', Professionalism in medicine', 'Professionalism of medical students', 'Professionalism in healthcare'. 'Teaching professionalism', and 'Attributes of professionalism'.
Results: After 2 rounds of screening by the subject experts and critical analysis of all the 137 YouTube videos, only 41 (29.92%) were identified as pertinent to the subject matter, i.e., educational type. After on expert viewing these 41 videos established upon our pre-set inclusion/exclusion criteria, only 17 (41.46%) videos were found to be academically valuable in nature.
Conclusion: Medical professionalism multimedia videos uploaded by the healthcare specialists or organizations on YouTube provided reliable information for medical students, healthcare workers and other professional. We conclude that YouTube is a leading and free online source of videos meant for students or other healthcare workers yet the viewers need to be aware of the source prior to using it for training learning.
METHODS: Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.
RESULTS: Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.
CONCLUSIONS: Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
METHODS: A total of 135 students from three undergraduate year levels of the MBBS degree at UAMC, Dhaka, Bangladesh, undertook study tours (community-based teaching, CBT) as a part of a community medicine course and visited a medical college, two rural health centres and a meteorology centre in the Cox's Bazar district, 400 km from Dhaka city. A questionnaire was used to assess the perceptions of students regarding the administration, organisation and learning experiences of the study tours. Students were required to write reports, present their findings and answer questions in their examinations related to the study tours and CBT.
RESULTS: The majority of the students agreed or strongly agreed that the tour was a worthwhile (93%) and enjoyable (95%) learning experience that helped them to understand rural health issues (91%). More than half of the students reported that the study tours increased their awareness about common rural health problems (54%) and provided a wider exposure to medicine (61%). Only 41% of students reported that the study tour increased their interest in undertake training in a rural area. A substantial number of students also expressed their concerns about the planning, length, resources, finance and organisation of the study tours.
CONCLUSIONS: Overall, the study tours had a positive effect, enhancing students' awareness and understanding of common rural health problems. As study tours failed to increase the motivation of the students (approximately 60%) to work in rural areas, CBT in the medical curriculum should be reviewed and implemented using effective and evidence-based models to promote interest among medical students to work in rural and underserved or unserved areas.