Overview on Updates on Digital Dental Radiography

Wilhelm Roentgen first discovered X-rays in 1895. Since its introduction in the mid-1980s, digital radiography has surpassed traditional screen-film radiography. Since 2000, more than 75 percent of medical clinics in the United States have migrated to digital radiography (DR). In fact, the US government has ordered that all medical records be converted to digital. Indirect, direct, or semi-direct digital radiography pictures are types of digital radiography currently available. Forensic radiology is a branch of medical imaging technology that helps clinicians. Radiology technology has evolved and grown tremendously in recent years. When comparing aggregated antemortem and postmortem information, radiographs are crucial. Adopting new technologies into a dental business demands a certain amount of bravery. After all, why alter things if your practise is running smoothly? To grasp the new equipment and procedures, the dentist and his or her staff will need further training. It's not always apparent how the new strategy will influence the practice's present logistics. These factors may cause the practitioner to be hesitant to alter present techniques. In this article we’ll reviewing digital dental radiography, and what are the advantages of going digital. And also what are the challenges that face us.

Detecting 17 Fine-Grained Dental Anomalies from Panoramic Dental Radiography using Artificial Intelligence

Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients’ health. However, it is time-consuming and laborious to detect all signs of anomalies because these regions are very complicated. So it is needed to filter out healthy images to save clinicians’ time to examine. For this, we applied modern artificial intelligence-based computer vision techniques. In this study, we built a model to detect 17 fine-grained dental anomalies which are critical to patients’ dental health and quality of life. We used about 23,000 anonymized panoramic dental images taken from local dental clinics from July 2020 to July 2021. Our model can detect these abnormal signs and filter out normal images with high sensitivity of about 0.99. The result indicates that our model can be used in real clinical practice to alleviate the burden of clinicians.

Increasing Radiation Exposure in Routine Dental Radiography as a Possible Side Effect of COVID-19 Pandemic: An Experimental Report

The article's abstract is not available.

Dental radiography and regulation

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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

Current applications and development of artificial intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.