Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry—A Narrative Review

Journal of Clinical Medicine, Journal Year: 2023, Volume and Issue: 12(23), P. 7378 - 7378

Published: Nov. 28, 2023

The concept of machines learning and acting like humans is what meant by the phrase “artificial intelligence” (AI). Several branches dentistry are increasingly relying on artificial intelligence (AI) tools. literature usually focuses AI models. These models have been used to detect diagnose a wide range conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous alveolar bone loss, need for orthodontic extractions or treatments, cephalometric analysis, age gender determination, more. primary contemporary applications in field undergraduate teaching research. Before these methods can be everyday dentistry, however, underlying technology user interfaces refined.

Language: Английский

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Deep Learning in Oral Hygiene: Automated Dental Plaque Detection via YOLO Frameworks and Quantification Using the O’Leary Index

Diagnostics, Journal Year: 2025, Volume and Issue: 15(2), P. 231 - 231

Published: Jan. 20, 2025

Background: Oral diseases such as caries, gingivitis, and periodontitis are highly prevalent worldwide often arise from plaque. This study focuses on detecting three plaque stages—new, mature, over-mature—using state-of-the-art YOLO architectures to enhance early intervention reduce reliance manual visual assessments. Methods: We compiled a dataset of 531 RGB images 177 individuals, captured via multiple mobile devices. Each sample was treated with disclosing gel highlight types, then preprocessed for lighting color normalization. YOLOv9, YOLOv10, YOLOv11, in various scales, were trained detect categories, their performance evaluated using precision, recall, mean Average Precision (mAP@50). Results: Among the tested models, YOLOv11m achieved highest mAP@50 (0.713), displaying superior detection over-mature Across all variants, older generally easier than newer plaque, which can blend gingival tissue. Applying O’Leary index indicated that over half population exhibited severe levels. Conclusions: Our findings demonstrate feasibility automated advanced models varied imaging conditions. approach offers potential optimize clinical workflows, support diagnoses, mitigate oral health burdens low-resource communities.

Language: Английский

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Detection of Fractured Endodontic Instruments in Periapical Radiographs: A Comparative Study of YOLOv8 and Mask R-CNN

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 653 - 653

Published: March 7, 2025

Background/Objectives: Accurate localization of fractured endodontic instruments (FEIs) in periapical radiographs (PAs) remains a significant challenge. This study aimed to evaluate the performance YOLOv8 and Mask R-CNN detecting FEIs root canal treatments (RCTs) compare their diagnostic capabilities with those experienced endodontists. Methods: A data set 1050 annotated PAs was used. models were trained evaluated for FEI RCT detection. Metrics including accuracy, intersection over union (IoU), mean average precision at 0.5 IoU (mAP50), inference time analyzed. Observer agreement assessed using inter-class correlation (ICC), comparisons made between AI predictions human annotations. Results: achieved an accuracy 97.40%, mAP50 98.9%, 14.6 ms, outperforming speed mAP50. demonstrated 98.21%, 95%, 88.7 excelling detailed segmentation tasks. Comparative analysis revealed no statistically differences Conclusions: Both high reliability, comparable YOLOv8’s rapid detection make it particularly suitable real-time clinical applications, while excels precise segmentation. establishes strong foundation integrating into dental diagnostics, offering innovative solutions improve outcomes. Future research should address diversity explore multimodal imaging enhanced capabilities.

Language: Английский

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YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition

BMC Medical Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: July 11, 2024

Abstract Objectives In the interpretation of panoramic radiographs (PRs), identification and numbering teeth is an important part correct diagnosis. This study evaluates effectiveness YOLO-v5 in automatic detection, segmentation, deciduous permanent mixed dentition pediatric patients based on PRs. Methods A total 3854 PRs were labelled for using CranioCatch labeling program. The dataset was divided into three subsets: training ( n = 3093, 80% total), validation 387, 10% total) test 385, total). An artificial intelligence (AI) algorithm models developed. Results sensitivity, precision, F-1 score, mean average precision-0.5 (mAP-0.5) values 0.99, 0.98 respectively, to detection. mAP-0.5 0.98, segmentation. Conclusions can have potential detect enable accurate segmentation with dentition.

Language: Английский

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DeMambaNet: Deformable Convolution and Mamba Integration Network for High-Precision Segmentation of Ambiguously Defined Dental Radicular Boundaries

Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4748 - 4748

Published: July 22, 2024

The incorporation of automatic segmentation methodologies into dental X-ray images refined the paradigms clinical diagnostics and therapeutic planning by facilitating meticulous, pixel-level articulation both structures proximate tissues. This underpins pillars early pathological detection meticulous disease progression monitoring. Nonetheless, conventional frameworks often encounter significant setbacks attributable to intrinsic limitations imaging, including compromised image fidelity, obscured delineation structural boundaries, intricate anatomical constituents such as pulp, enamel, dentin. To surmount these impediments, we propose Deformable Convolution Mamba Integration Network, an innovative 2D architecture, which amalgamates a Coalescent Structural Encoder, Cognitively-Optimized Semantic Enhance Module, Hierarchical Convergence Decoder. Collectively, components bolster management multi-scale global features, fortify stability feature representation, refine amalgamation vectors. A comparative assessment against 14 baselines underscores its efficacy, registering 0.95% enhancement in Dice Coefficient diminution 95th percentile Hausdorff Distance 7.494.

Language: Английский

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Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches

Frontiers in Dental Medicine, Journal Year: 2025, Volume and Issue: 6

Published: May 20, 2025

Background Third molar extraction, a common dental procedure, often involves complications, such as alveolar nerve injury. Accurate preoperative assessment of the extraction difficulty and injury risk is crucial for better surgical planning patient outcomes. Recent advancements in deep learning (DL) have shown potential to enhance predictive accuracy using panoramic radiographic (PR) images. This systematic review evaluated reliability DL models predicting third inferior (IAN) risk. Methods A search was conducted across PubMed, Scopus, Web Science, Embase until September 2024, focusing on studies assessing complexity IAN PR The inclusion criteria required report performance metrics. Study selection, data quality were independently performed by two authors PRISMA QUADAS-2 guidelines. Results Six involving 12,419 images met criteria. demonstrated high (up 96%) 92.9%), with notable sensitivity 97.5%) specific classifications, horizontal impactions. Geographically, three originated South Korea one each from Turkey Thailand, limiting generalizability. Despite accuracy, demographic sparsely reported, only providing sex distribution. Conclusion show promise improving extraction. However, further validation diverse populations integration clinical workflows are necessary establish its real-world utility, limitations limited generalizability, selection bias lack long-term follow up remain challenges.

Language: Английский

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Fully automated deep learning framework for detection and classification of impacted mandibular third molars in panoramic radiographs

Journal of Oral Medicine and Oral Surgery, Journal Year: 2025, Volume and Issue: 31(1), P. 7 - 7

Published: Jan. 1, 2025

Introduction: Mandibular third molars (MTMs) are the most frequently impacted teeth, making their detection and classification essential before surgical extraction. This study aims to develop assess accuracy of a deep learning model for detecting classifying mandibular (IMTMs) using panoramic radiographs (PRs). Materials methods: The utilized dataset 1100 PRs with 1200 IMTMs 711 without MTMs. An oral radiologist validated annotations, data were split into training, validation, testing sets. Sobel Third Molar Detection Model (STMD), built on VGG16 architecture, identified Detected MTMs located YOLOv7 classified per Winter’s via ResNet50-based prediction model. Results: VGG16-based achieved 93.51%, precision 94.64, recall 89.47, an F1 score 91.97. attained 92.17%, 92.1, 92.17, AUC 98.28. These findings demonstrate high reliability both models. Conclusion: ResNet50 integrated YOLOv7, demonstrated suggesting that automatic can be significantly improved these

Language: Английский

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External Validation of the Effect of the Combined Use of Object Detection for the Classification of the C-Shaped Canal Configuration of the Mandibular Second Molar in Panoramic Radiographs: A Multicenter Study

Journal of Endodontics, Journal Year: 2024, Volume and Issue: 50(5), P. 627 - 636

Published: Feb. 8, 2024

Language: Английский

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i-Dent: A virtual assistant to diagnose rare genetic dental diseases

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 180, P. 108927 - 108927

Published: Aug. 2, 2024

Rare genetic diseases are difficult to diagnose and this translates in patient's diagnostic odyssey! This is particularly true for more than 900 rare including orodental developmental anomalies such as missing teeth. However, if left untreated, their symptoms can become significant disabling the patient. Early detection rapid management therefore essential context. The i-Dent project aims supply a pre-diagnostic tool detect with tooth agenesis of varying severity pattern. To identify teeth, image segmentation models (Mask R-CNN, U-Net) have been trained automatic teeth on patients' panoramic dental X-rays. Teeth enables identification which present or within mouth. Furthermore, age assessment conducted verify whether absence an anomaly characteristic age. Due small size our dataset, we developed new technique based eruption rate. Information about then used by final algorithm probabilities propose pre-diagnosis disease. results obtained detecting three types genes (PAX9, WNT10A EDA) system very promising, providing average accuracy 72 %.

Language: Английский

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An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 195, P. 105724 - 105724

Published: Nov. 23, 2024

Language: Английский

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