The use of drones has rapidly evolved over the past decade involving a variety of fields ranging from agriculture, commercial and becoming increasingly used in disaster management or humanitarian aid. Unfortunately, the evidence of its use in mass disasters is still unclear and scarce. This article aims to evaluate the current drone feasibility projects and to discuss a number of challenges related to the deployment of drones in mass disasters in the hopes of empowering and inspiring possible future work. This research follows Arksey and O'Malley framework and updated by Joanna Briggs Institute Framework for Scoping Reviews methodology to summarise the results of 52 research papers over the past ten years, from 2009 to 2020, outlining the research trend of drone application in disaster. A literature search was performed in Medline, CINAHL, Scopus, individual journals, grey literature and google search with assessment based on their content and significance. Potential application of drones in disaster are broad. Based on articles identified, drone application in disasters are classified into four categories; (1) mapping or disaster management which has shown the highest contribution, (2) search and rescue, (3) transportation and (4) training. Although there is a significant increase in the number of publications on use of drone in disaster within the last five years, there is however limited discussion to address post-disaster healthcare situation especially with regards to disaster victim identification. It is evident that drone applications need to be further explored; to focus more on drone assistance to humans especially in victim identification. It is envisaged that with sufficient development, the application of drones appears to be promising and will improve their effectiveness especially in disaster management.

Background: Forensic odontology may require a visual or clinical method during identification. Sometimes it may require forensic experts to refer to the existing technique to identify individuals, for example, by using the atlas to estimate the dental age. However, the existing technology can be a complicated procedure for a large-scale incident requiring a more significant number of forensic identifications, particularly during mass disasters. This has driven many experts to perform automation in their current practice to improve efficiency.Objective: This article aims to evaluate current artificial intelligence applications and discuss their performance concerning the algorithm architecture used in forensic odontology.Methods: This study summarizes the findings of 28 research papers published between 2010 and June 2022 using the Arksey and O'Malley framework, updated by the Joanna Briggs Institute Framework for Scoping Reviews methodology, highlighting the research trend of artificial intelligence technology in forensic odontology. In addition, a literature search was conducted on Web of Science (WoS), Scopus, Google Scholar, and PubMed, and the results were evaluated based on their content and significance.Results: The potential application of artificial intelligence technology in forensic odontology can be categorized into four: (1) human bite marks, (2) sex determination, (3) age estimation, and (4) dental comparison. This powerful tool can solve humanity's problems by giving an adequate number of datasets, the appropriate implementation of algorithm architecture, and the proper assignment of hyperparameters that enable the model to perform the prediction at a very high level of performance.Conclusion: The reviewed articles demonstrate that machine learning techniques are reliable for studies involving continuous features such as morphometric parameters. However, machine learning models do not strictly require large training datasets to produce promising results. In contrast, deep learning enables the processing of unstructured data, such as medical images, which require large volumes of data. Occasionally, transfer learning was used to overcome the limitation of data. In the meantime, this method's capacity to automatically learn task-specific feature representations has made it a significant success in forensic odontology.

Background: This study aims to propose the combinations of image processing and machine learning model to segment the maturity development of the mandibular premolars using a Keras-based deep learning convolutional neural networks (DCNN) model. Methods: A dataset consisting of 240 images (20 images per stage per sex) of retrospect digital dental panoramic imaging of patients between 5 and 14 years of age was retrieved. In image preprocessing, abounding box with a dimension of 250 × 250 pixels was assigned to the left mandibular first (P1) and second (P2) permanent premolars. The implementation of dynamic programming of active contour (DP-AC) and convolutions neural network on images that require the procedure of image filtration using Python TensorFlow and Keras libraries were performed in image segmentation and classification, respectively. Results: Image segmentation using the DP-AC algorithm enhanced the visibility of the image features in the region of interest while suppressing the image's background noise. The proposed model has an accuracy of 97.74%, 96.63% and 78.13% on the training, validation, and testing set, respectively. In addition, moderate agreement (Kappa value = 0.58) between human observer and computer were identified. Nonetheless, a robust DCNN model was achieved as there is no sign of the model's over-or under-fitting upon the learning process. Conclusions: The application of digital imaging and deep learning techniques used by the DP-AC and convolutions neural network algorithms to segment and identify premolars provides promising results for semi-automated forensic dental staging in the future.

Background: The research shows a growing trend in using an electronic platform to supplement or replace traditional paper-based informed consent processes. Instead of the traditionally written informed consent document, electronic informed consent (eConsent) may be used to assess the research subject’s comprehension of the information presented. By doing so, respect for persons as one of the research ethical principles can be upheld. Furthermore, these electronic methods may reduce potential airborne infection exposures, particularly during the pandemic, thereby adhering to the beneficence and nonmaleficence principle. This scoping review aims to identify the ethics related criteria that have been included in electronic informed consent processes and to synthesize and map these criteria to research ethics principles, in order to identify the gaps, if any, in current electronic informed consent processes.Methods: The search was performed based on internet search and three main databases: PubMed, SCOPUS and EBSCO. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation guideline was used to report this work.Results: Of 34 studies that met the inclusion criteria, 242 essential original constructs were collated, and 7 concepts were derived. Digital content showed the highest percentage of collated original constructs (27%, n = 65) followed by accessibility (24%, n = 56), comprehension engagement (18%, n = 43), autonomy (14%, n = 34), confidentiality (11%, n = 25), language (5%, n = 13), and parental consent (1%, n = 2). Twenty-five new items were synthesized for eConsent criteria which may provide guidance for ethical review of research involving eConsent.Conclusion: The current study adds significant value to the corpus of knowledge in research ethics by providing ethical criteria on electronic informed consent based on evidence-based data. The new synthesized items in the criteria can be readily used as an initial guide by the IRB/REC members during a review process on electronic informed consent and useful to the future preparation of a checklist.