Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

AbuSalim, Samah; Zakaria, Nordin; Islam, Rafiqul; Kumar, Ganesh; Mokhtar, Norehan; Abdulkadir, Said Jadid

doi:10.3390/healthcare10101892

Cited by 25 publications

(12 citation statements)

References 166 publications

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“…These variations make it challenging to classify the teeth accurately. Hence analysis of dental images using deep learning models has caught the attention of many researchers [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

AbuSalim¹,

Zakaria²,

Mostafa³

et al. 2023

IJACSA

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In image classification, multi-granularity refers to the ability to classify images with different levels of detail or resolution. This is a challenging task because the distinction between subcategories is often minimal, needing a high level of visual detail and precise representation of the features specific to each class. In dental informatics, and more specifically tooth classification poses many challenges due to overlapping teeth, varying sizes, shapes, and illumination levels. To address these issues, this paper considers various data granularity levels since a deeper level of details can be acquired with increased granularity. Three tooth granularity levels are considered in this study named Two Classes Granularity Level (2CGL), Four Classes Granularity Level (4CGL), and Seven Classes Granularity Level (7CGL) to analyze the performance of teeth detection and classification at multi-granularity levels in Granular Intra-Oral Image (GIOI) dataset. Subsequently, a Faster Region-Convolutional Neural Network (FR-CNN) based on three ResNet models is proposed for teeth detection and classification at multi-granularity levels from the GIOI dataset. The FR-CNN-ResNet models exploit the effect of the tooth classification granularity technique to empower the models with accurate features that lead to improved model performance. The results indicate a remarkable detection effect in investigating the granularity effect on the FR-CNN-ResNet model's performance. The FR-CNN-ResNet-50 model achieved 0.94 mAP for 2CGL, 0.74 mAP for 4CGL, and 0.69 mAP for 7CGL, respectively. The findings demonstrated that multi-granularity enables flexible and nuanced analysis of visual data, which can be useful in a wide range of applications.

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“…These variations make it challenging to classify the teeth accurately. Hence analysis of dental images using deep learning models has caught the attention of many researchers [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

AbuSalim¹,

Zakaria²,

Mostafa³

et al. 2023

IJACSA

View full text Add to dashboard Cite

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“…Unlike some machine learning models that act as a “black box,” random forests provide insights into which features are essential in predicting dental caries. This interpretability can guide dentists in understanding risk factors and tailoring patient preventive measures [ 31 , 32 ].…”

Section: Reviewmentioning

confidence: 99%

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques

Anil¹,

Porwal²,

Porwal³

2023

Cureus

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Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.

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“…Most of the dentistry review studies [31][32][33][34][35][36][37] in recent years have not included the latest deep learning techniques such as Transformers, 21 GCNs, 38 GANs, 39 weak annotations, 40 and other techniques for the latest applications in dental segmentation. In addition, they did not provide a detailed classification and summary of deep learning-based methods for dental image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based tooth segmentation methods in medical imaging: A review

Chen,

Ma,

et al. 2024

Proc Inst Mech Eng H

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Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.

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Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review

Cited by 25 publications

References 166 publications

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques

Deep learning-based tooth segmentation methods in medical imaging: A review

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