Transfer Learning-Based Classifier to Automate the Extraction of False X-Ray Images From Hospital's Database.

BACKGROUND: During preclinical training, dental students take radiographs of acrylic (plastic) blocks containing extracted patient teeth. With the digitisation of medical records, a central archiving system was created to store and retrieve all x-ray images, regardless of whether they were images of teeth on acrylic blocks, or those from patients. In the early stage of the digitisation process, and due to the immaturity of the data management system, numerous images were mixed up and stored in random locations within a unified archiving system, including patient record files. Filtering out and expunging the undesired training images is imperative as manual searching for such images is problematic. Hence the aim of this stidy was to differentiate intraoral images from artificial images on acrylic blocks. METHODS: An artificial intelligence (AI) solution to automatically differentiate between intraoral radiographs taken of patients and those taken of acrylic blocks was utilised in this study. The concept of transfer learning was applied to a dataset provided by a Dental Hospital. RESULTS: An accuracy score, F1 score, and a recall score of 98.8%, 99.2%, and 100%, respectively, were achieved using a VGG16 pre-trained model. These results were more sensitive compared to those obtained initally using a baseline model with 96.5%, 97.5%, and 98.9% accuracy score, F1 score, and a recall score respectively. CONCLUSIONS: The proposed system using transfer learning was able to accurately identify "fake" radiographs images and distinguish them from the real intraoral images.

The Effect of Dental Implant Drills Materials on Heat Generation in Osteotomy Sites: A Systematic Review.

The aim of this review was to examine the impact of dental implant drill materials and wear profiles on heat generation in the osteotomy sites as reported in experimental studies and to critically appraise these studies. The research question was formulated based on predefined patient, intervention, comparison, and outcome (PICO) elements. A comprehensive electronic search was undertaken in Medline/PubMed Central, Science Direct, and Google Scholar, using predetermined keywords, followed by a manual search of the bibliography of the selected articles. The selection of the studies for the critical appraisal part of our study was based on the criteria used to assess the study designs such as study aims, outcome measure, clarity of method, sample selection, randomization, allocation concealment, sample attrition, bias, method of data analysis, and external validity. Increased heat generation was observed with both ceramic and metal drills; the heat generation was proportional to drills' wear. The literature was inconclusive regarding the association between drill material and heat generation. However, drill materials had a significant influence on the overall temperature increase during osteotomy. The noncoated drills showed a higher wear resistance, and it has been observed that using worn drills leads to more friction contact, reduced drill cutting efficiency, and increased heat generation. Eleven in vitro studies met the inclusion criteria, and showed a considerable methodological heterogeneity and confounding factors, including drill geometry, speed and load, depth and diameter, number of uses, irrigation protocol, study specimens, and the heat measuring device. Besides, most of the studies have a potential operator and assessor bias, and some have sponsorship bias. It is possible to conclude that the literature is not conclusive on the effect of drill materials on heat generation during osteotomy. Lack of standardization and uniformity in the study design, along with potential bias in the study methodology can be the reason for the heterogeneity of the results.

Developing an Artificial Intelligence Solution to Autosegment the Edentulous Mandibular Bone for Implant Planning.

OBJECTIVE: Dental implants are considered the optimum solution to replace missing teeth and restore the mouth's function and aesthetics. Surgical planning of the implant position is critical to avoid damage to vital anatomical structures; however, the manual measurement of the edentulous (toothless) bone on cone beam computed tomography (CBCT) images is time-consuming and is subject to human error. An automated process has the potential to reduce human errors and save time and costs. This study developed an artificial intelligence (AI) solution to identify and delineate edentulous alveolar bone on CBCT images before implant placement. MATERIALS AND METHODS: After obtaining the ethical approval, CBCT images were extracted from the database of the University Dental Hospital Sharjah based on predefined selection criteria. Manual segmentation of the edentulous span was done by three operators using ITK-SNAP software. A supervised machine learning approach was undertaken to develop a segmentation model on a "U-Net" convolutional neural network (CNN) in the Medical Open Network for Artificial Intelligence (MONAI) framework. Out of the 43 labeled cases, 33 were utilized to train the model, and 10 were used for testing the model's performance. STATISTICAL ANALYSIS: The degree of 3D spatial overlap between the segmentation made by human investigators and the model's segmentation was measured by the dice similarity coefficient (DSC). RESULTS: The sample consisted mainly of lower molars and premolars. DSC yielded an average value of 0.89 for training and 0.78 for testing. Unilateral edentulous areas, comprising 75% of the sample, resulted in a better DSC (0.91) than bilateral cases (0.73). CONCLUSION: Segmentation of the edentulous spans on CBCT images was successfully conducted by machine learning with good accuracy compared to manual segmentation. Unlike traditional AI object detection models that identify objects present in the image, this model identifies missing objects. Finally, challenges in data collection and labeling are discussed, together with an outlook at the prospective stages of a larger project for a complete AI solution for automated implant planning.

The Potential of Blockchain Technology in Dental Healthcare: A Literature Review.

Blockchain technology in the healthcare industry has potential to enable enhanced privacy, increased security, and an interoperable data record. Blockchain technology is being implemented in dental care systems to store and share medical information, improve insurance claims, and provide innovative dental data ledgers. Because the healthcare sector is a large and ever-growing industry, the use of blockchain technology would have many benefits. To improve dental care delivery, researchers advocate using blockchain technology and smart contracts due to their numerous advantages. In this research, we concentrate on blockchain-based dental care systems. In particular, we examine the current research literature, pinpoint issues with existing dental care systems, and consider how blockchain technology may be used to address these issues. Finally, the limitations of the proposed blockchain-based dental care systems are discussed which may be regarded as open issues.

Introducing a comprehensive high-stake online exam to final-year dental students during the COVID-19 pandemic and evaluation of its effectiveness.

BACKGROUND: Dental education involves teaching and assessing the acquisition of verifiable domains that require superior psychomotor, communication, and cognitive skills. Evolving technologies and methods of assessment could enhance student learning environment and improve tutor assessment experience. OBJECTIVE: The aim of this study was to introduce the application of a comprehensive high-stakes online exam to final-year dental students during the COVID-19 pandemic and evaluate its effectiveness. DESIGN: A high-stakes exam was introduced and implemented online to the final-year dental students prior to their graduation. The exam consisted of four components: MEQs, MCQs, OSCE and an oral exam. The exam and invigilation were conducted using Blackboard and MS Teams programs. Stakeholders' views of the exam were obtained using two tailored surveys, one for students and another for faculty; both included closed- and open-ended questions. RESULTS: The exam was run successfully without untoward events. Both students and staff were satisfied with the online exam with the latter being more satisfied than the former. Students with previous experience in online learning system were more satisfied with the online exam compared with those with less experience (p < 0.05). The main issues raised by students' satisfaction with the exam were: inadequacy of time for the MEQ part, prevention of back tracking in the MCQ part and minor technological issues, whereas those raised by faculty members were increased time required to complete the exam setup and grading compared to the paper-based exam and minor technological issues. CONCLUSIONS: A newly introduced, multi-format, online high-stakes exam was implemented successfully to final-year dental students with minor technological issues and good satisfaction by students and staff alike.