Background: Clear aligner therapy (CAT) offers aesthetic and hygienic advantages over traditional fixed orthodontic appliances. However, its uptake is influenced by patients' knowledge, attitudes, perceptions (KAP) and acceptance of the treatment. This study aimed to evaluate the KAP towards CAT among patients undergoing fixed orthodontic treatment and to identify demographic and socioeconomic factors influencing these attitudes. Methods: A cross-sectional survey was conducted at a private hospital in Lagos, Nigeria, involving all consenting follow-up orthodontic patients undergoing fixed appliance orthodontic therapy. A validated self-administered questionnaire assessed demographic characteristics and KAP toward CAT. Data were analysed using SPSS v26 and R software, employing chi-square and logistic regression. Results: Among 232 participants, only 50% had heard of CAT, and just 27.6% demonstrated good knowledge. About 52.2% showed a positive attitude, and 50.9% had a favourable perception. Significant associations were found between attitude and age (p = 0.022), education (p = 0.030), ethnicity (p = 0.035), and income (p = 0.013). Income uncertainty was a negative predictor of a favourable attitude (OR = 0.42, p = 0.025). Conclusion: Knowledge of CAT among patients treated with fixed appliances is limited, despite moderate awareness and positive attitudes. Educational and economic factors significantly influence acceptance. Improved education and affordability could enhance uptake of aligner therapy.

Background: Root fractures represent a relatively rare form of dental trauma and are often challenging to identify using routine clinical examination and conventional radiographic techniques. Accurate and timely diagnosis is crucial for appropriate treatment planning and to achieve favourable clinical outcomes. In recent years, artificial intelligence (AI) has gained attention in dentistry due to its ability to analyze imaging data with high precision and assist clinicians in diagnostic decision-making. Purpose: The aim of this systematic review is to assess the role and diagnostic effectiveness of artificial intelligence in identifying root fractures. Study selection: A systematic literature search was performed using PUBMED, MEDLINE, EMBASE, and COCHRANE Library with language restriction to English. The search was carried out incorporating the published literature till 2025 using the MeSH (medical subject heading) terms. A literature search was done out of 205 publications, related to search strategy, 57 full articles, which were related to the study, were acquired for further inspection. Out of the 57 articles, 9 articles met the inclusion criteria. Information related to study characteristics, types of AI models used, imaging techniques, and reported diagnostic performance was collected and reviewed. Results: The findings from the 9 selected studies indicate that AI systems, especially deep learning models such as convolutional neural networks, demonstrate considerable potential in detecting root fractures in dental images. Many investigations reported strong diagnostic performance with notable levels of accuracy, sensitivity, and specificity. These findings highlight the significant potential of AI-assisted analysis helped improve diagnostic consistency and supported clinicians in recognizing fractures that may be difficult to detect through visual assessment alone. Conclusion and Relevance: Artificial intelligence shows significant promise as a supportive diagnostic tool for the detection of root fractures. Despite the encouraging results, further well-designed studies with larger datasets and clinical validation are required before AI technologies can be widely integrated into routine dental practice. Artificial intelligence enhances the accuracy and consistency of root fracture detection, aiding clinicians in early and reliable diagnosis. Its integration into dental imaging can reduce diagnostic errors and support timely treatment decisions.