Panoramic Dental X-ray Images Research Articles

DentAge: Deep learning for automated age prediction using panoramic dental X-ray images.

Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X-ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine-tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10-20]) to 13.40 years (age group [90-100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real-world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios.

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.

CariesXrays: Enhancing Caries Detection in Hospital-Scale Panoramic Dental X-rays via Feature Pyramid Contrastive Learning

Dental caries has been widely recognized as one of the most prevalent chronic diseases in the field of public health. Despite advancements in automated diagnosis across various medical domains, it remains a substantial challenge for dental caries detection due to its inherent variability and intricacies. To bridge this gap, we release a hospital-scale panoramic dental X-ray benchmark, namely “CariesXrays”, to facilitate the advancements in high-precision computer-aided diagnosis for dental caries. It comprises 6,000 panoramic dental X-ray images, with a total of 13,783 instances of dental caries, all meticulously annotated by dental professionals. In this paper, we propose a novel Feature Pyramid Contrastive Learning (FPCL) framework, that jointly incorporates feature pyramid learning and contrastive learning within a unified diagnostic paradigm for automated dental caries detection. Specifically, a robust dual-directional feature pyramid network (D2D-FPN) is designed to adaptively capture rich and informative contextual information from multi-level feature maps, thus enhancing the generalization ability of caries detection across different scales. Furthermore, our model is augmented with an effective proposals-prototype contrastive regularization learning (P2P-CRL) mechanism, which can flexibly bridge the semantic gaps among diverse dental caries with varying appearances, resulting in high-quality dental caries proposals. Extensive experiments on our newly-established CariesXrays benchmark demonstrate the potential of FPCL to make a significant social impact on caries diagnosis.

Dental panoramic X-ray image segmentation for multi-feature coordinate position learning.

To achieve an accurate assessment of orthodontic and restorative treatments, tooth segmentation of dental panoramic X-ray images is a critical preliminary step, however, dental panoramic X-ray images suffer from poorly defined interdental boundaries and low root-to-alveolar bone contrast, which pose significant challenges to tooth segmentation. In this article, we propose a multi-feature coordinate position learning-based tooth image segmentation method for tooth segmentation. For better analysis, the input image is randomly flipped horizontally and vertically to enhance the data. Our method extracts multi-scale tooth features from the designed residual omni-dimensional dynamic convolution and the designed two-stream coordinate attention module can further complement the tooth boundary features, and finally the two features are fused to enhance the local details of the features and global contextual information, which achieves the enrichment and optimization of the feature information. The publicly available adult dental datasets Archive and Dataset and Code were used in the study. The experimental results were 87.96% and 92.04% for IoU, 97.79% and 97.32% for ACC, and 86.42% and 95.64% for Dice. The experimental results show that the proposed network can be used to assist doctors in quickly viewing tooth positions, and we also validate the effectiveness of the proposed two modules in fusing features.

Spatial Prior-Guided Bi-Directional Cross-Attention Transformers for Tooth Instance Segmentation.

Tooth instance segmentation of dental panoramic X-ray images represents a task of significant clinical importance. Teeth demonstrate symmetry within the upper and lower jawbones and are arranged in a specific order. However, previous studies frequently overlook this crucial spatial prior information, resulting in misidentifications of tooth categories for adjacent or similarly shaped teeth. In this paper, we propose SPGTNet, a spatial prior-guided transformer method, designed to both the extracted tooth positional features from CNNs and the long-range contextual information from vision transformers for dental panoramic X-ray image segmentation. Initially, a center-based spatial prior perception module is employed to identify each tooth's centroid, thereby enhancing the spatial prior information for the CNN sequence features. Subsequently, a bi-directional cross-attention module is designed to facilitate the interaction between the spatial prior information of the CNN sequence features and the long-distance contextual features of the vision transformer sequence features. Finally, an instance identification head is employed to derive the tooth segmentation results. Extensive experiments on three public benchmark datasets have demonstrated the effectiveness and superiority of our proposed method in comparison with other state-of-the-art approaches. The proposed method demonstrates the capability to accurately identify and analyze tooth structures, thereby providing crucial information for dental diagnosis, treatment planning, and research.

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisionlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images’ binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.

Enhancing teeth segmentation using multifusion deep neural net in panoramic X-ray images.

Precise teeth segmentation from dental panoramic X-ray images is an important task in dental practice. However, several issues including poor image contrast, blurring borders of teeth, presence of jaw bones and other mouth elements, makes reading and examining such images a challenging and time-consuming task for dentists. Thus, developing a precise and automated segmentation technique is required. This study aims to develop and test a novel multi-fusion deep neural net consisting of encoder-decoder architecture for automatic and accurate teeth region segmentation from panoramic X-ray images. The encoder has two different streams based on CNN which include the conventional CNN stream and the Atrous net stream. Next, the fusion of features from these streams is done at each stage to encode the contextual rich information of teeth. A dual-type skip connection is then added between the encoder and decoder to minimise semantic information gaps. Last, the decoder comprises deconvolutional layers for reconstructing the segmented teeth map. The assessment of the proposed model is performed on two different dental datasets consisting of 1,500 and 1,000 panoramic X-ray images, respectively. The new model yields accuracy of 97.0% and 97.7%, intersection over union (IoU) score of 91.1% and 90.2%, and dice coefficient score (DCS) of 92.4% and 90.7% for datasets 1 and 2, respectively. Applying the proposed model to two datasets outperforms the recent state-of-the-art deep models with a relatively smaller number of parameters and higher accuracy, which demonstrates the potential of the new model to help dentists more accurately and efficiently diagnose dental diseases in future clinical practice.

Unsupervised knowledge representation of panoramic dental X-ray images using SVG image-and-object clustering

Unsupervised knowledge representation of panoramic dental X-ray images using SVG image-and-object clustering

ResMIBCU-Net: an encoder-decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images.

Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years, major advances have been made in medical imaging segmentation using deep convolutional neural network-based networks. In this study, we report on the development of an artificial intelligence system for the automatic identification of impacted tooth from panoramic dental X-ray images. Among existing networks, in medical imaging segmentation, U-Net architectures are widely implemented. In this article, for dental X-ray image segmentation, blocks and convolutional block structures using inverted residual blocks are upgraded by taking advantage of U-Net's network capacity-intensive connections. At the same time, we propose a method for jumping connections in which bi-directional convolution long short-term memory is used instead of a simple connection. Assessment of the proposed artificial intelligence model performance was evaluated with accuracy, F1-score, intersection over union, and recall. In the proposed method, experimental results are obtained with 99.82% accuracy, 91.59% F1-score, 84.48% intersection over union, and 90.71% recall. Our findings show that our artificial intelligence system could help with future diagnostic support in clinical practice.

Deep learning for automatic mandible segmentation on dental panoramic x-ray images

Many studies in the last decades have correlated mandible bone structure with systemic diseases like osteoporosis. Mandible segmentation, as well as segmentation of other oral structures, is an essential step in studies that correlate oral structures’ conditions with systemic diseases in general. However, manual mandible segmentation is a time-consuming and training-required task that suffers from inter and intra-user variability. Further, the dental panoramic x-ray image (PAN), the most used image in oral studies, contains overlapping of many structures and lacks contrast on structures’ interface. Those facts make both manual and automatic mandible segmentation a challenge. In the present study, we propose a precise and robust set of deep learning-based algorithms for automatic mandible segmentation (AMS) on PAN images. Two datasets were considered. An in-house image dataset with 393 image/segmentation pairs was prepared using image data of 321 image patient data and the corresponding manual segmentation performed by an experienced specialist. Additionally, a publicly available third-party image dataset (TPD) composed of 116 image/segmentation pairs was used to train the models. Four deep learning models were trained using U-Net and HRNet architectures with and without data augmentation. An additional morphological refinement routine was proposed to enhance the models’ prediction. An ensemble model was proposed combining the four best-trained segmentation models. The ensemble model with morphological refinement achieved the highest scores on the test set (98.27%, 97.60%, 97.18%, ACC, DICE, and IoU respectively), with the other models scoring above 95% in all performance metrics on the test set. The present study achieved the highest ranked performance considering all the previously published results on AMS for PAN images. Additionally, those are the most robust results achieved since it was performed over an image set with considerable gender representativeness, a wide age range, a large variety of oral conditions, and images from different imaging scans.

Exploiting multimodal CNN architecture for automated teeth segmentation on dental panoramic X-ray images.

Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods.

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even. Image-based decisions are commonly taken through explicit or auto-feature engineering of images. In forensic radiology, auto decisions based on images significantly affect the automation of various tasks. This study aims to assist forensic radiology in its biological profile estimation when only bones are left. A benchmarked dataset Radiology Society of North America (RSNA) has been used for research and experiments. Additionally, a locally developed dataset has also been used for research and experiments to cross-validate the results. A Convolutional Neural Network (CNN)-based model named computer vision and image processing-net (CVIP-Net) has been proposed to learn and classify image features. Experiments have also been performed on state-of-the-art pertained models, which are alex_net, inceptionv_3, google_net, Residual Network (resnet)_50, and Visual Geometry Group (VGG)-19. Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender. The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy (98.90%), specificity (97.99%), sensitivity (99.34%), and Area under the Curve (AUC)-value (0.99) on the locally developed dataset to detect age. The classification rates of the proposed model for gender estimation were 99.57%, 97.67%, 98.99%, and 0.98, achieved in terms of accuracy, specificity, sensitivity, and AUC-value, respectively, on the local dataset. The classification rates of the proposed model for age estimation were 96.80%, 96.80%, 97.03%, and 0.99 achieved in terms of accuracy, specificity, sensitivity, and AUC-value, respectively, on the RSNA dataset.

Dental Panoramic Radiography in Age Estimation for Dental Care using Dark-Net 19

A rapidly increasing human lifespan has led to population aging with growing demands for healthcare, including dental healthcare services. The oral cavity plays a crucial role in mastication and speech. Furthermore, artificial intelligence (AI) has extensively developed, and automated diagnostic technology continues to evolve. Therefore, it is necessary to integrate objective AI, automatic diagnosis, and dental care to reduce errors resulting from subjective diagnosis. This study used 15,000 dental panoramic X-ray images to train an AI algorithm and estimate dental age, using Darknet-19 for the AI model. The accuracy was about 84 % and 96 %, respectively, when the dental estimate acceptable range was ± 5 and ± 10 years. Based on this model, patients could evaluate their oral condition and improve their quality of life using appropriate treatment and prevention methods.

Teeth U-Net: A segmentation model of dental panoramic X-ray images for context semantics and contrast enhancement

Background and objectiveIt is very significant in orthodontics and restorative dentistry that the teeth are segmented from dental panoramic X-ray images. Nevertheless, there are some problems in panoramic X-ray images of teeth, such as blurred interdental boundaries, low contrast between teeth and alveolar bone. MethodsIn this paper, The Teeth U-Net model is proposed in this paper to resolve these problems. This paper makes the following contributions: Firstly, a Squeeze-Excitation Module is utilized in the encoder and the decoder. And proposing a dense skip connection between encoder and decoder to reduce the semantic gap. Secondly, due to the irregular shape of the teeth and the low contrast of the dental panoramic X-ray images. A Multi-scale Aggregation attention Block (MAB) in the bottleneck layer is designed to resolve this problem, which can effectively extract teeth shape features and fuse multi-scale features adaptively. Thirdly, in order to capture dental feature information in a larger field of perception, this paper designs a Dilated Hybrid self-Attentive Block (DHAB) at the bottleneck layer. This module effectively suppresses the task-irrelevant background region information without increasing the network parameters. Finally, the effectiveness of the algorithm is validated using a clinical dental panoramic X-ray image datasets. ResultsThe results of the three comparison experiments are shown that Accuracy, Precision, Recall, Dice, Volumetric Overlap Error and Relative Volume Difference for dental panoramic X-ray teeth segmentation are 98.53%, 95.62%, 94.51%, 94.28%, 88.92% and 95.97% by the proposed model respectively. ConclusionThe proposed modules complement each other in processing every detail of the dental panoramic X-ray images, which can effectively improve the efficiency of preoperative preparation and postoperative evaluation, and promote the application of dental panoramic X-ray in medical image segmentation. There are more accuracy about Teeth U-Net than others model in dental panoramic X-ray teeth segmentation. That is very important to clinical doctors to cure in orthodontics and restorative dentistry.

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images.

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

Tooth detection for each tooth type by application of faster R-CNNs to divided analysis areas of dental panoramic X-ray images.

This study aimed to propose a computerized method for detecting the tooth region for each tooth type as the initial stage in the development of a computer-aided diagnosis (CAD) scheme for dental panoramic X-ray images. Our database consists of 160 panoramic dental X-ray images obtained from 160 adult patients. To reduce false positives (FPs), the proposed method first extracts a rectangular area including all teeth from a dental panoramic X-ray image with a faster region using a convolutional neural network (Faster R-CNN). From the rectangular area including all teeth, six divided areas are then extracted with Faster R-CNN: top left, top center, top right, bottom left, bottom center, and bottom right. Faster R-CNNs for detecting tooth regions for each tooth type were trained individually for each of the divided areas that narrowed down the target tooth types. By applying these Faster R-CNNs to each divided area, the bounding boxes of each tooth were detected and classified into 32 tooth types. A k-fold cross-validation method with k = 4 was used for training and testing the proposed method. The detection rate for each tooth, number of FPs per image, mean intersection over union for each tooth, and classification accuracy for the 32 tooth types were 98.9%, 0.415, 0.748, and 91.7%, respectively, showing an improvement compared to the application of the Faster R-CNN once to the entire image (98.0%, 1.194, 0.736, and 88.8%).

Automated estimation of chronological age from panoramic dental X-ray images using deep learning

Age estimation is a key component in forensic analysis, be it in legal proceedings or archeological research. Current methods in forensic odontology are based on manual measurements of a wide array of morphometric parameters, typically from dental x-ray images, and occasionally from material remains. While those parameters follow a set progression during human development, thereby allowing current methods to precisely estimate the age of juveniles, estimation for adults and seniors proves to be more difficult. In this study, we explore the applicability of deep learning to the problem of chronological age estimation. We determine the best convolutional neural network model derived from state-of-the-art architectures, we determine the best performing model parameters using pretrained general-purpose vision model parameters as the starting point, and we perform ablation experiments to highlight which anatomical regions of the dental system contribute the most to the estimation. The proposed approach attains the lowest estimation error in literature for adult and senior subjects, which we verify on one of the largest datasets of panoramic dental x-ray images in literature. The dataset consists of 4035 panoramic dental x-ray images of male and female subjects with ages between 19 and 90 years. This study also evaluates the feasibility of the proposed model for age estimations of individual teeth, achieving an estimation error competitive with current methods while being fully automated. The estimation error is verified on our dataset of 76416 individual tooth images, which is the largest dataset to date in forensic odontology literature. Unlike current methods, dental alterations, decay, illnesses, or missing teeth do not pose a problem to the proposed model. With a median estimation error of 2.95 years for panoramic dental x-ray images and 4.68 years for individual teeth, and by deriving the model from state-of-the-art architectures, verifying those results on the largest dataset in forensic odontology literature and demonstrating the importance of different anatomical regions of the dental system for estimation, this study sets the baseline for future research of automated chronological age estimation in forensic odontology.

LCANet: Learnable Connected Attention Network for Human Identification Using Dental Images.

Forensic odontology is regarded as an important branch of forensics dealing with human identification based on dental identification. This paper proposes a novel method that uses deep convolution neural networks to assist in human identification by automatically and accurately matching 2-D panoramic dental X-ray images. Designed as a top-down architecture, the network incorporates an improved channel attention module and a learnable connected module to better extract features for matching. By integrating associated features among all channel maps, the channel attention module can selectively emphasize interdependent channel information, which contributes to more precise recognition results. The learnable connected module not only connects different layers in a feed-forward fashion but also searches the optimal connections for each connected layer, resulting in automatically and adaptively learning the connections among layers. Extensive experiments demonstrate that our method can achieve new state-of-the-art performance in human identification using dental images. Specifically, the method is tested on a dataset including 1,168 dental panoramic images of 503 different subjects, and its dental image recognition accuracy for human identification reaches 87.21% rank-1 accuracy and 95.34% rank-5 accuracy. Code has been released on Github. (https://github.com/cclaiyc/TIdentify).

MSLPNet: multi-scale location perception network for dental panoramic X-ray image segmentation

Tooth segmentation, as one of the key techniques of medical image segmentation, can be widely applied to various medical applications, e.g., orthodontic treatment, corpse identification, dental training systems, dental disease diagnosis, etc. Although there have been many studies on tooth segmentation, few tooth segmentation studies have focused on enhancing tooth segmentation with fuzzy root boundaries which is a difficult but essential task in dentistry to determine the root resorption and the tooth brace. The existing methods for tooth segmentation usually exploited the contrast enhancement to sharpen the boundaries of teeth, while the final segmentation results heavily depended on the subsequent processing steps of the method. To address the issue of fuzzy boundaries, this paper proposes a novel multi-scale location perception network to segment teeth from panoramic X-ray images. The core of the proposed method stems from three aspects: (1) multi-scale structural similarity loss conducts accurate prediction with clear boundary from patch-level scale; (2) location perception module locates each tooth pixel in the image from the perspective of global-level scale; (3) aggregation module reduces the semantic gap between multi-scale feature branches. Our proposed method was tested on a dataset containing 1500 dental panoramic X-ray images and the Dice score of our method is 93.01%, which outperforms the state-of-the-art approaches. Besides, our proposed method has better boundary quality and the Pratt (1978)’s figure of merit used for boundary quality evaluation reaches 76.56%.

Object detection on dental x-ray images using deep learning method

Radiological examination has an important role in determining the diagnosis of dental problems and making decisions about the right type of treatment according to the case indications. Dental x-ray is a medical procedure for taking pictures of the inside of the mouth using radiation fluid, where the results are used diagnostically to help the dentist see the entire structure of the jaw bone and teeth, and dental problems that cannot be seen directly. Dental radiographic interpretation, which is generally performed by dentists, is a time-consuming and error-prone process due to high variations in tooth structure, low experience levels, and fatigue factors experienced by dentists. The workload of a dentist and the occurrence of misdiagnosis can be reduced by the existence of a system that can automatically interpret the x-ray results. To overcome these problems, a model will be developed to be able to detect objects in the dental panoramic x-ray images using Mask R-CNN, one of the methods in Deep Learning. Deep Learning is an artificial intelligence function that modelled the workings of human brain in processing data and creating patterns for use in decision making. With the detection of objects in panoramic x-ray image automatically, it is expected to save time, improve the quality of dental care, and also the quality of diagnosis made by dentists.