Microstructural Evaluation of Dental Implant Success Using Micro-CT: A Comprehensive Review DOI Creative Commons

Krisnadi Setiawan,

Risti Saptarini Primarti, Suhardjo Sitam

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11016 - 11016

Published: Nov. 27, 2024

Micro-computed tomography (micro-CT) is an invaluable tool for the evaluation of dental implant success, whereby assessment bone microstructure conducted. This review examines role micro-CT in evaluating implants. A current literature reveals that enables accurate measurement volume, trabecular morphology, and connectivity density, all which play a crucial stability. The high-resolution three-dimensional visualization capabilities are also beneficial analysis osseointegration augmentation biomaterials. Despite existence challenges such as imaging artifacts limitations vivo applications, advancements sub-micron resolution artificial intelligence integration offer promise improving diagnostic capabilities. Micro-CT provides valuable insights into microarchitecture dynamics, have potential to enhance pre-operative planning clinical outcomes implantology. Future research should prioritize standardization protocols exploration direct applications this technology.

Language: Английский

Application of artificial intelligence in dental crown prosthesis: a scoping review DOI Creative Commons
Hyun-Jun Kong,

YuLee Kim

BMC Oral Health, Journal Year: 2024, Volume and Issue: 24(1)

Published: Aug. 13, 2024

In recent years, artificial intelligence (AI) has made remarkable advancements and achieved significant accomplishments across the entire field of dentistry. Notably, efforts to apply AI in prosthodontics are continually progressing. This scoping review aims present applications performance dental crown prostheses related topics. We conducted a literature search PubMed, Scopus, Web Science, Google Scholar, IEEE Xplore databases from January 2010 2024. The included articles addressed application various aspects treatment, including fabrication, assessment, prognosis. initial electronic yielded 393 records, which were reduced 315 after eliminating duplicate references. inclusion criteria led analysis 12 eligible publications qualitative review. AI-based this detection finish line, evaluation color matching, preparation, designed by AI, identification an intraoral photo, prediction debonding probability. potential increase efficiency processes such as fabricating evaluating crowns, with high level accuracy reported most analyzed studies. However, number studies focused on designing crowns using software, these had small patients did not always their algorithms. Standardized protocols for reporting needed evidence effectiveness.

Language: Английский

Citations

7
Dental implant planning using artificial intelligence: A systematic review and meta-analysis DOI
Ahmed Yaseen Alqutaibi,

Radhwan S. Algabri,

Wafaa Ibrahim

et al.

Journal of Prosthetic Dentistry, Journal Year: 2024, Volume and Issue: unknown

Published: April 1, 2024

Language: Английский

Citations

6
Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset DOI Creative Commons
Jae‐Hong Lee, Young‐Taek Kim, Jong‐Bin Lee

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 1, 2024

Abstract Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted radiographs, limiting their actual clinical use. This study aimed evaluate the effectiveness of an AI model, trained on a large multi-center dataset, in identifying different types DIS radiographs. Based fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic periapical radiological images were used as training validation datasets, 530 four (including those not perpendicular axis fixture, radiation overexposure, cut off apex containing foreign bodies) test datasets. Moreover, accuracy performance classification was compared using five periodontists. evaluation model achieved accuracy, precision, recall, F1 score metrics 95.05%, 95.91%, 92.49%, 94.17%, respectively. However, periodontists performed nine DISs based achieving mean overall 37.2 ± 29.0%. Within limitations this study, demonstrated superior from or outperforming professionals tasks. for application AI, extensive standardization research radiographic is essential.

Language: Английский

Citations

6
Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning DOI Creative Commons

Mohammed Al-Asali,

Ahmed Yaseen Alqutaibi, Mohammed Al-Sarem

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 16, 2024

Abstract Recent studies have shown that dental implants high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, been effectively applied analyze medical and images. This study aims utilize models segment bone in regions where teeth are missing cone-beam computerized tomography (CBCT) scans predict the positions of implants. The proposed were CBCT dataset Taibah University Dental Hospital (TUDH) patients between 2018 2023. They evaluated using different performance metrics validated by domain expert. experimental results demonstrated outstanding terms dice, precision, recall for segmentation (0.93, 0.94, 0.93, respectively) with low volume error (0.01). offer promising automated implant planning implantologists.

Language: Английский

Citations

6
Artificial Intelligence demonstrates potential in detecting caries on bitewing radiographs, but further high-quality studies are required DOI
Ahmed Yaseen Alqutaibi,

Anas Saeed AL-ZAGHRURI

Journal of Evidence Based Dental Practice, Journal Year: 2025, Volume and Issue: 25(1), P. 102087 - 102087

Published: Jan. 5, 2025

Language: Английский

Citations

0
Artificial Intelligence‐Based Detection and Numbering of Dental Implants on Panoramic Radiographs DOI Creative Commons
Yunus Balel, Kaan Sağtaş, Fatih Teke

et al.

Clinical Implant Dentistry and Related Research, Journal Year: 2025, Volume and Issue: 27(1)

Published: Jan. 23, 2025

ABSTRACT Objectives This study aimed to develop an artificial intelligence (AI)‐based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty this lies its ability both detect number implants, offering improvements clinical decision support implantology. Materials Methods A retrospective dataset 32 585 radiographs, collected from patients at Sivas Cumhuriyet University between 2014 2024, was utilized. Two deep‐learning models were trained using YOLOv8 algorithm. first classified regions jaw teeth identify implant regions, while second performed segmentation. Performance metrics including precision, recall, F1‐score used evaluate model's effectiveness. Results segmentation achieved a precision 91.4%, recall 90.5%, 93.1%. For implant‐numbering task, ranged 0.94 0.981, 0.895 0.956, F1‐scores 0.917 0.966 across various regions. analysis revealed that most frequently located maxillary posterior region. Conclusions AI demonstrated high accuracy detecting technology offers potential reduce clinicians' workload improve diagnostic Further validation more diverse datasets is recommended enhance applicability. Clinical Relevance could revolutionize classification, providing fast, objective analyses decision‐making practices.

Language: Английский

Citations

0
A Comprehensive Analysis of the Radiographic Characteristics and Bilateral Symmetry of the Mental Foramen DOI Creative Commons
Radwan Algabri,

Faisal Abulohoom,

Abdelrahman Fadag

et al.

Clinical and Experimental Dental Research, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 1, 2025

ABSTRACT Objectives There is currently a scarcity of data on the frequency and bilateral symmetry position other characteristics mental foramen (MF) accessory foramina in Yemen. The objective this study was to analyze characteristics, as well MF, sample Yemeni population. Materials Methods A retrospective analysis conducted 500 digital panoramic radiographs (1000 sides). examined various including horizontal vertical positions, shapes, appearances, presence foramina. Additionally, explored potential associations between these variables such subject's gender, sides, symmetry. Data performed using SPSS, statistical significance evaluated chi‐square tests; p value set at 0.05. Results MF most frequently observed first second lower premolars (63.2%). predominantly below apices (66.2%). majority MFs had round shape (46.3%). In 72% 75.6% cases, there continuous descending relationship mandibular canal, respectively. Accessory present 3.8% cases. Gender differences were significant for pattern canal right side. rates features included positions (87.4%), (82.6%), shapes (80.4%). Conclusion commonly situated horizontally vertically teeth. showed with canal. instances, symmetrical both sides.

Language: Английский

Citations

0
Advancements of artificial intelligence algorithms in predicting dental implant prognosis from radiographic images: A systematic review DOI
Ahmed Yaseen Alqutaibi,

Radhwan S. Algabri,

Abdulrahman S Alamri

et al.

Journal of Prosthetic Dentistry, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

1
Artificial Intelligence for Dental Implant Classification and Peri-Implant Pathology Identification in 2D Radiographs: A Systematic Review DOI Creative Commons
Mónica Bonfanti‐Gris, Edwin Ruales‐Carrera, María Paz Salido

et al.

Journal of Dentistry, Journal Year: 2024, Volume and Issue: unknown, P. 105533 - 105533

Published: Dec. 1, 2024

Language: Английский

Citations

1
Application of artificial intelligence in dental crown prosthesis: A scoping review DOI
Hyun-Jun Kong,

Yu Lee Kim

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 12, 2024

Abstract Background This scoping review aims to present the applications and performance of artificial intelligence (AI) in dental crown prostheses related topics. Methods We conducted a literature search PubMed, Scopus, Web Science, Google Scholar, IEEE Xplore databases from January 2010 2024. The included articles addressed application AI various aspects treatment, including fabrication, assessment, prognosis. Results initial electronic yielded 393 records, which were reduced 315 after eliminating duplicate references. inclusion criteria led analysis 12 eligible publications qualitative review. AI-based this detection finish line, evaluation color matching, preparation, designed by AI, identification an intraoral photo, prediction debonding probability. Conclusions has potential increase efficiency processes such as fabricating evaluating crowns, with high level accuracy reported most analyzed studies. However, significant number studies focused on designing crowns using software, these had small patients did not always their algorithms. Standardized protocols for reporting are needed evidence effectiveness.

Language: Английский

Citations

0