DESIGN: Single-centre prospective two-arm parallel randomised controlled trial.
SETTING: Orthodontic Clinic, Faculty of Dentistry, Universiti Teknologi MARA, Selangor, Malaysia.
PARTICIPANTS: Adult orthodontic patients aged 18-35 years, indicated for DPT and LC, who were fit and healthy with a body mass index of 18.5-25.0, not contraindicated to radiographic examination, not pregnant, and did not have a history of facial or skeletal abnormalities or bone diseases were included.
METHODS: Thirty-eight adult orthodontic patients were randomised into control and intervention groups. DPT and LC radiographs in the control group were obtained using standard scanning parameters as prescribed by the manufacturer using Orthopantomograph® OP300 by Instrumentarium. Scanning parameters in the intervention group were reduced by 60% for DPT (60 kV, 3.2 mA) and 30% for LC (85 kV, 8 mA). A five-point rating scale was used for the assessment of image quality. Images were evaluated for diagnostic performance by detection of anatomical landmarks. Mann-Whitney test was performed to compare the quality and diagnostic performance of the images and the observer agreement was assessed using the intraclass correlation coefficient (ICC).
RESULTS: For image quality, the control group produced slightly lower median scores (DPT 2.0, LC 2.0) compared to the intervention group (DPT 2.0, LC 3.0). For diagnostic performance, both groups showed similar median scores (DPT 21.0, LC 32.0). The differences between control and intervention groups for both modalities were not statistically significant. The average scores for intra-observer agreement were excellent (ICC 0.917) and inter-observer agreement was good (ICC 0.822).
CONCLUSION: Minimising radiation exposure by reducing scanning parameters on digital DPT by 60% and LC by 30% on Intsrumentarium 300 OP did not affect the quality and diagnostic performance of the images. Thus, scanning parameters on digital DPT and LC should be reduced when taking radiographs.
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.