Harnessing the Power of an Integrated Artificial Intelligence Model for Enhancing Reliable and Efficient Dental Healthcare Systems

Applied System Innovation, Journal Year: 2025, Volume and Issue: 8(1), P. 7 - 7

Published: Jan. 2, 2025

Nowadays, efficient dental healthcare systems are considered significant for upholding oral health. Also, the ability to utilize artificial intelligence evaluating complex data implies that X-ray image recognition is a critical mechanism enhance disease detection. Consequently, integrating deep learning algorithms into promising approach enhancing reliability and efficiency of diagnostic processes. In this context, an integrated model proposed performance interpretability. The basic idea augment with Ensemble methods improve accuracy robustness healthcare. model, Non-Maximum Suppression (NMS) ensembled technique employed predictions along combining outputs from multiple single models (YOLO8 RT-DETR) make final decision. Experimental results on real-world datasets show gives high in miscellaneous diseases. achieves 18% time reductions as well 30% improvements compared other competitive algorithms. addition, effectiveness achieved 74% mAP50 58% mAP50-90, outperforming existing models. Furthermore, grants degree system reliability.

Language: Английский

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Multidisciplinary Applications of AI in Dentistry: Bibliometric Review

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(17), P. 7624 - 7624

Published: Aug. 28, 2024

This review explores the impact of Artificial Intelligence (AI) in dentistry, reflecting on its potential to reshape traditional practices and meet increasing demands for high-quality dental care. The aim this research is examine how AI has evolved dentistry over past two decades, driven by pivotal questions: “What are current emerging trends developments dentistry?” implications do these have future field?”. Utilizing Scopus database, a bibliometric analysis literature from 2000 2023 was conducted address inquiries. findings reveal significant increase AI-related publications, especially between 2018 2023, underscoring rapid expansion applications that enhance diagnostic precision treatment planning. Techniques such as Deep Learning (DL) Neural Networks (NN) transformed enhancing reducing workload. technologies, particularly Convolutional (CNNs) (ANNs), improved accuracy radiographic analysis, detecting pathologies automating cephalometric evaluations, thereby optimizing outcomes. advocacy underpinned need be both efficacious ethically sound, ensuring they not only improve clinical outcomes but also adhere highest standards patient

Language: Английский

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Informatic tools for diagnosis in dentistry. A compilation review

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

The objective of this study was to compile the computer tools available in scientific literature aimed at helping diagnosis dentistry. A scoping review conducted using PubMed, Scopus, and Web Science. Were include articles that reported usefulness a computer/technological tool helps dental practice, published last 20 years English Spanish. Online Rayyan® used establish homogeneity authors centralize results. In total, 12648 records were retrieved from databases. After decantation, 39 reports described 36 help for More informatic related "Restorative Dentistry’ have been developed than rest specialties 14 (40%). Python predominant programming language, 83.3% validated, 27.8% free. Informatics dentistry enhance treatment planning. However, robust regulatory framework is required validation prior clinical implementation. Continuous training professionals these technologies crucial maximize their benefits ensure optimal patient care. research needed explore potential informatics applications dentistry, integration into existing health systems, accessibility resource-limited areas.

Language: Английский

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A systematic literature review: exploring the challenges of ensemble model for medical imaging

BMC Medical Imaging, Journal Year: 2025, Volume and Issue: 25(1)

Published: April 18, 2025

Medical imaging has been essential and provided clinicians with useful information about the human body to diagnose various health issues. Early diagnosis of diseases based on medical can mitigate risk severe consequences enhance long-term outcomes. Nevertheless, task diagnosing be challenging due exclusive ability interpret outcomes imaging, which is time-consuming susceptible fallibility. The ensemble model potential accuracy diagnoses by analyzing vast volumes data identifying trends that may not immediately apparent doctors. However, it takes a lot memory processing resources train maintain several models. These challenges highlight necessity effective scalable models manage intricacies assignments. This study employed an SLR technique explore latest advancements approaches. By conducting thorough systematic search Scopus Web Science databases in accordance principles outlined PRISMA, employing keywords namely imaging. included total 75 papers were published between 2019 2024. categorization, methodologies, use key factors examined analysis 30 cited this study, focus diseases. Researchers have observed emergence for disease using since demonstrated improved guide future studies highlighting limitations model.

Language: Английский

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AI-Driven Dental Caries Management Strategies: From Clinical Practice to Professional Education and Public Self Care

International Dental Journal, Journal Year: 2025, Volume and Issue: 75(4), P. 100827 - 100827

Published: May 10, 2025

Language: Английский

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YOLOv8 Model Architecture Selection for Human Fall Detection

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 219 - 227

Published: Jan. 1, 2025

Language: Английский

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Influence of Intraoral Scanners, Operators, and Data Processing on Dimensional Accuracy of Dental Casts for Unsupervised Clinical Machine Learning: An In Vitro Comparative Study

International Journal of Dentistry, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 10

Published: Nov. 22, 2023

This study assessed the impact of intraoral scanner type, operator, and data augmentation on dimensional accuracy in vitro dental cast digital scans. It also evaluated validation an unsupervised machine-learning model trained with these Twenty-two casts were scanned using two handheld scanners one laboratory scanner, resulting 110 3D scans across five independent groups. The underwent uniform validated Hausdorff's distance (HD) root mean squared error (RMSE), as reference. A 3-factor analysis variance examined interactions between scanners, operators, methods. Scans divided into training sets processed through a pretrained visual transformer, was for each No significant differences HD RMSE found operators. However, changes observed native augmented no specific interaction or operator. transformer achieved 96.2% differentiating upper lower dataset. Native lacked volumetric depth, preventing their use deep learning. Scanner, processing method did not significantly affect crucial learning algorithms, introducing structural Clinical Significance. type operator has substantial influence quality generated scans, but controlled is necessary to obtain reliable results

Language: Английский

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

Journal of Ambient Intelligence and Humanized Computing, Journal Year: 2024, Volume and Issue: 15(7), P. 3009 - 3033

Published: April 21, 2024

Language: Английский

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Toward the Development of an Oral-diagnosis Framework: A Case Study of Teeth Segmentation and Numbering in Bitewing Radiographs via YOLO Models

Published: March 29, 2024

This study presents an oral-diagnosis framework integrating the YOLOv8 model for precise tooth localization in dental imaging. The segmentation and numbering right-side bitewing radiographic images were evaluated through comparison of YOLOv5 models, employing confidence thresholds. dataset comprised 800 training 152 testing images, with architecture deployed three variants. Precision, recall, F1-score, mean average precision (mAP) both models. demonstrated superior performance over (0.913 vs. 0.897), F1-score (0.931 0.920), mAP (0.96 0.954). Variations dimensions observed among S, M, L variants, marginal improvements specific classes. In conclusion, while did not enhance tasks across varying sizes, it consistently outperformed YOLOv5, exhibiting detection abilities.

Language: Английский

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Mapping the Use of Artificial Intelligence–Based Image Analysis for Clinical Decision‐Making in Dentistry: A Scoping Review

Clinical and Experimental Dental Research, Journal Year: 2024, Volume and Issue: 10(6)

Published: Nov. 26, 2024

ABSTRACT Objectives Artificial intelligence (AI) is an emerging field in dentistry. AI gradually being integrated into dentistry to improve clinical dental practice. The aims of this scoping review were investigate the application image analysis for decision‐making and identify trends research gaps current literature. Material Methods This followed guidelines provided by Preferred Reporting Items Systematic Reviews Meta‐Analyses Extension Scoping (PRISMA‐ScR). An electronic literature search was performed through PubMed Scopus. After removing duplicates, a preliminary screening based on titles abstracts performed. A full‐text according predefined inclusion criteria, data extracted from eligible articles. Results Of 1334 articles returned, 276 met criteria (consisting 601,122 images total) included qualitative synthesis. Most studies utilized convolutional neural networks (CNNs) radiographs such as orthopantomograms (OPGs) intraoral (bitewings periapicals). applied across all fields ‐ particularly oral medicine, surgery, orthodontics direct inference segmentation. AI‐based use several components process, including diagnosis, detection or classification, prediction, management. Conclusions variety machine learning deep techniques are used assist clinicians making accurate diagnoses choosing appropriate interventions timely manner.

Language: Английский

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