METHODOLOGY: Jaw sections containing 67 teeth (86 roots) were collected from nine fresh, unclaimed bodies that were due for cremation. Imaging was carried out to detect AP lesions using film and digital PR with a centred view (FP and DP groups); film and digital PR combining central with 10˚ mesially and distally angled (parallax) views (FPS and DPS groups). All specimens underwent histopathological examination to confirm the diagnosis of AP. Sensitivity, specificity and predictive values of PR were analysed using rater mean (n = 5). Receiver operating characteristics (ROC) analysis was carried out.
RESULTS: Sensitivity was 0.16, 0.37, 0.27 and 0.38 for FP, FPS, DP and DPS, respectively. Both FP and FPS had specificity and positive predictive values of 1.0, whilst DP and DPS had specificity and positive predictive values of 0.99. Negative predictive value was 0.36, 0.43, 0.39 and 0.44 for FP, FPS, DP and DPS, respectively. Area under the curve (AUC) for the various imaging methods was 0.562 (FP), 0.629 (DP), 0.685 (FPS), 0.6880 (DPS).
CONCLUSIONS: The diagnostic accuracy of single digital periapical radiography was significantly better than single film periapical radiography. The inclusion of two additional horizontal (parallax) angulated periapical radiograph images (mesial and distal horizontal angulations) significantly improved detection of apical periodontitis.
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.