Browsing by Author "Elawady, Dina"

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Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis
(Mosby Inc, 2024-01) Alqutaibi, Ahmed Yaseen; Algabri, Radhwan S; Elawady, Dina; Ibrahim, Wafaa Ibrahim
Abstract Statement of problem: The evidence regarding the application of artificial intelligence (AI) in identifying dental implant systems is currently inconclusive. The available studies present varying results and methodologies, making it difficult to draw definitive conclusions. Purpose: The purpose of this systematic review with meta-analysis was to comprehensively analyze and evaluate articles that investigate the application of AI in identifying and classifying dental implant systems. Material and methods: An electronic systematic review was conducted across 3 databases: MEDLINE/PubMed, Cochrane, and Scopus. Additionally, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies investigating the accuracy of AI-based diagnostic tools on dental radiographs for identifying and classifying dental implant systems and comparing the results with those obtained by expert judges using manual techniques—the search strategy encompassed articles published until September 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included articles. Results: Twenty-two eligible articles were included in this review. These articles described the use of AI in detecting dental implants through conventional radiographs. The pooled data showed that dental implant identification had an overall accuracy of 92.56% (range 90.49% to 94.63%). Eleven studies showed a low risk of bias, 6 demonstrated some concern risk, and 5 showed a high risk of bias. Conclusions: AI models using panoramic and periapical radiographs can accurately identify and categorize dental implant systems. However, additional well-conducted research is recommended to identify the most common implant systems.
Dental implant planning using artificial intelligence: A systematic review and meta-analysis
(Elsevier Inc, 2024-04) Alqutaibi, Ahmed Yaseen; Algabri, Radhwan; Ibrahim, Wafaa Ibrahim; Alhajj, Mohammed Nasser; Elawady, Dina
Statement of problem: Data on the role of artificial intelligence (AI) in dental implant planning is insufficient. Purpose: The purpose of this systematic review with meta-analysis was to analyze and evaluate articles that assess the effectiveness of AI algorithms in dental implant planning, specifically in detecting edentulous areas and evaluating bone dimensions. Material and methods: A systematic review was conducted across the MEDLINE/PubMed, Web of Science, Cochrane, and Scopus databases. In addition, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies that examined the accuracy of AI-based diagnostic tools on dental radiographs for dental implant planning. The most recent search was conducted in January 2024. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of the included articles. Results: Twelve articles met the inclusion criteria for this review and focused on the application of AI in dental implant planning using cone beam computed tomography (CBCT) images. The pooled data indicated an overall accuracy of 96% (95% CI=94% to 98%) for the mandible and 83% (95% CI=82% to 84%) for the maxilla in identifying edentulous areas for implant planning. Eight studies had a low risk of bias, 2 studies had some concern of bias, and 2 studies had a high risk of bias. Conclusions: AI models have the potential to identify edentulous areas and provide measurements of bone as part of dental implant planning using CBCT images. However, additional well-conducted research is needed to enhance the accuracy, generalizability, and applicability of AI-based approaches.
Single Implant-Retained Mandibular Overdentures: A Literature Review
(2024-01) Elawady, Dina; Adam, Maya Ali; Allam, Hamzah; Mahmoud, Ihab Ismail; Alqutaibi, Ahmed Yaseen; Shon, Ahmed Atef
The absence of teeth, known as edentulism, poses considerable obstacles in prosthodontic care and greatly affects a person's well-being. Conventional complete dentures frequently lead to problems like instability and insufficient retention, especially in the lower jaw. Fortunately, the introduction of dental implants has transformed the way we approach edentulous patients, as they now offer support and enhanced retention for removable prostheses, thus revolutionizing their treatment. While a consensus exists on using two implants for retaining mandibular overdentures, the associated cost may be prohibitive for economically disadvantaged individuals. As a solution, the concept of single implant-retained mandibular overdentures has emerged, catering to individuals with limited financial resources and complete tooth loss. This review explores the efficacy and suitability of the single implant overdenture approach, along with an overview of treatment options for edentulous patients, including traditional dentures, tooth-supported overdentures, and implant-supported overdentures. The preservation of bone, improvements in functional abilities, and psychological benefits associated with overdentures are discussed. Moreover, various classifications and prosthetic options for implant overdentures, specifically for mandibular cases, are presented. This literature review aims to provide a comprehensive understanding of possible treatment options and focus on the single implant-retained mandibular overdenture approach and its implications in prosthodontic rehabilitation for edentulous patients.