Incidence of various dental anomalies in population of Taxilla, Pakistan

Background: There are different types of teeth anomalies that effects the people of different regional populations. Aim: To determine the occurrence of dental anomalies in patients of Taxila that visit our college for routine dental procedures. Methods: The study was retrospective and was conducted on periapical intraoral radiographs of patients between the ages of 15 to 35 years, with no gender discrimination at Dental College HITEC-IMS. Results: We collected data from 450 periapical intraoral radiographs that were taken in last six months (i.e. 15th January 2021 till 15th July 2021) in dental radiology department. Conclusion: The dental anomalies that were found in the population of taxila were impacted teeth, missing teeth, rotated tooth, supernumerary teeth (mesiodens), root dilacerations, peg lateral, taurodontism and hypercementosis. Keywords: Root anomalies, dental anomalies, periapical radiograph

Effects of mobile learning on writing panoramic radiograph reports: a quasi-experimental trial in dental education

Background In dentistry, the reporting of panoramic radiographs is particularly challenging, as many structures are depicted in one image and pathologies need to be identified completely. To enhance the learning process for these interpretations, the advantages of the increasingly popular education method of mobile learning could be used. Therefore, this study aimed to determine the effectiveness of learning to report panoramic radiographs using an application (app) on a mobile device. Methods The existing e-learning programme ‘PantoDict’ was further developed into a mobile app with a new training section. Participants of a dental radiology course were divided into two groups, one of which additionally had the chance to practise reporting panoramic radiographs using the app. A test to assess the knowledge gained was conducted at the end of the semester; the course and the app were also evaluated. Results The group that used the app showed significantly better results in the test than the control group (p < 0.05). Although the app group approved a high satisfaction using the app as an additional supplement to the course, this did not result in a higher overall satisfaction with the course. Further, these students observed that the traditional face-to-face seminar could not be replaced by the app. Conclusion By using the PantoDict app, students were offered better training options for writing reports on panoramic radiographs, which resulted in significantly better test results than the results of the control group. Therefore, the mobile app is a useful supplement to classical education formats within the context of a blended learning approach.

Readability of Online Patient Education Materials for Dental Radiology

Conclusion: None of the webpages fulfilled the recommendations from the National Institute of Health and the American Medical Association of being written below a seventh-grade level. More online patient education materials for dental radiology were recommended, and they should be written in a more easily understood way.

Assessment of Knowledge and Perspective toward Dental Radiography among Dental Practitioners of Sindh Province Pakistan

BACKGROUND Radiography is one of the important tools that dentists use to diagnose dental diseases in the oral cavity. Exposure of radiation is associated with hazardous effects on oral tissues. Doctors must have enough knowledge regarding the consequences of radiation exposure. The purpose of this study was to assess knowledge and perspective of dental practitioners towards dental radiography. METHODS This descriptive cross-sectional study was conducted in the month of November and December 2020, among dental practitioners who worked in the state of Sindh. All the participants were given the questionnaire survey link through social media including Facebook, WhatsApp, and Email and 24 closed ended questions were asked regarding dental radiography. A total of 247 dental practitioners responded and participated in the study. RESULTS Our study showed that only 3.60 % of general practitioners (GP) reported that they did not have radiographic unit. The participant’s knowledge regarding the technical details of equipment was limited. Majority of dental practitioners preferred long cone, more than 50 % specialist recommended F-speed of film. 34.53 % of general dentists and 37.73 % of specialists responded that they have digital radiography. More than 50 % of dental practitioners didn’t have license for x-ray equipment. Majority of them utilized paralleling technique for periapical x-ray. 63.40 % of GP and only 11.32 % of specialist held x-ray film with the fingers when taking x-ray. 30.41 % of GP and 24.52 % of specialist took the radiographs themselves, whereas 35.05 % had x-ray done by technician. Only 1.54 % of GP and 3.775 % of specialists gave the radiographic packing materials to specialized company in order to discard the waste materials. Only 6.70 % of GP and 11.32 % of specialists had the walls of the x-ray room covered with lead. CONCLUSIONS This study concluded that dental practitioners have little knowledge regarding dental radiography. KEY WORDS Knowledge, Perspective, Dental Practitioners, Dental Radiology

Is artificial intelligence really a future trend in health care?

Everyone who uses any digital platform in the daily routine has already been surprised by some sudden ad or product advertisement about which some information has been sought on the Internet. Coincidence? Of course not! This is just one example of how artificial intelligence is inserted into our daily lives. It is in the platforms for music streaming, movies, shopping for any product, in traffic applications, in the stock market. Each "like", each share, each post shows a pattern of consumer preference, a characteristic that can be used to direct advertisements in order to advertise or market a product to a specific target. This is already happening, it is not part of the future. Artificial intelligence is already part of our present. But how do these platforms manage to "guess" our preferences or tastes and hit exactly what we were looking for? In reality nothing is guessed, it is learned. Through computer modeling, these systems learn from the examples that we ourselves give them. We feed these systems on a daily basis. Just like children, who learn many things by example (languages, for instance) before they even go to school, these systems are also capable of learning. A child learns that a dog is different from a cat when it sees examples of several dogs and several cats. So a child can learn the differences between both animals. Algorithms learn the same way, through examples. This is what we call "machine learning," a sub-area of artificial intelligence (AI). It is an advance for society, but it must be applied with ethics and transparency (see the Netflix documentary Coded Bias). Moving away from the market sphere and thinking about health care, machine learning has also been widely employed, because these systems have the ability to learn using endless amount of patient and hospital data (Big Data). In this sense, AI-based systems have been developed aiming at improving patient care, from the organization of triage systems at clinics and hospitals, patient scheduling, organization of test result delivery, preventing errors in drug prescriptions, as well as predicting and assisting in disease diagnosis. The artificial intelligence literature in the medical field is already vast. In dentistry, research has focused on the use of convolutional neural networks (CNN) in dental radiology. Tools are produced for researchers and system developers that aim at assisting clinicians in imaging diagnosis, for example, of dental caries, periapical lesions, bone resorption, among other important outcomes. Some companies, in Brazil and worldwide, have already seen a potential market in the application of these neural networks, and are providing software to assist in the analysis of radiographic images. Far from being able to replace health professionals, this technology should be used to improve the work of dentists and bring more security in diagnosis. Trying to replace a health professional with artificial intelligence, especially in dentistry, is impossible and not productive at all (see Eric Topol's book Deep Medicine). Information technology as an ally will bring many benefits to dentistry, not only in radiology. The analysis of digital cohorts (electronic patient records) with machine learning algorithms can bring new insights to Science. Such algorithms are able to cross-reference thousands of predictive attributes with various endpoints to define which information is most relevant for qualitative analyses. It is the new advanced statistics. For this reason, it is especially important to emphasize the need to build a large-scale public dental dataset to make the clinical application of AI possible. The challenge now is to improve the quality of the datasets to build really accurate machine learning algorithms. Finally, it would be very useful for dentists if these developed machine learning systems become applications that could be widely available and spread to the dental community. The spectrum of AI is huge! Try doing a search today on some topic and wait for the algorithm to work! It will offer you all the information, based on the search example you yourself have offered! This is AI in our lives, no future, but a present! Keywords Artificial intelligence; Health care.

Viewing patterns regarding panoramic radiographs with different pathological lesions: an eye-tracking study

Objective: The aim of this study was to examine how dental students vary their viewing patterns of panoramic radiographs during different levels of dental education. Methods: Two groups of students (total number = 48, n = 24) in different grades (second and fifth clinical semester) were compared. The second clinical semester participated twice, as during the second clinical semester a specific lecture on dental radiology and diagnosis is held. The first viewing took place at the beginning of the semester (2a), the second at the end of it (2e). The fifth semester (5e) represents students shortly before graduation. While viewing 20 panoramic radiographs showing specific pathologies the eye movement was captured by an eye-tracker. After a maximum of 60 sec per image the students had to report a suspected diagnosis. Every panoramic radiograph included a pathologic lesion which was diagnosed by an expert observer who also defined the areas of interest (AOI). The images were presented in the same order to each participant. The metric data recorded by the tracking-system included total time to first fixation, total fixation count, total gaze duration and coordinates of the fixation in and outside an area of interest. In addition, parameters like the completeness of scanning and the suspected diagnosis were analyzed. Differences between the groups were assessed for statistical significance and associations between level of different grades, viewing time, completeness of scanning and correctness of diagnosis were computed. Results: 2e was significantly faster (p < 0,001), whereas 5e was significantly (p < 0.001) more likely to diagnose correctly and also to scan more completely. Scanning duration did not significantly influence the correctness of diagnosis. The lower edges of the panoramic radiographs were not scanned as often as the center of the image. Bony lesions were generally found to be difficult to interpret and significant findings located in the sinus were overlooked the most. Conclusion: The higher semester had a more complete viewing pattern and diagnosed correctly with a higher percentage. After hearing the mentioned lecture, the second semester scanned faster and mentioned the AOI more often but could not make a right diagnosis.