Image texture in dental panoramic radiographs as a potential biomarker of osteoporosis

Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis

The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.

Y. Strut and radio-morphometric analysis of mandibular trabecular structure in pre-and post-menopausal women to aid in the diagnosis of osteoporosis

Purpose

The purpose of the study is to evaluate the mandibular trabecular pattern in pre- and postmenopausal age women. By analysing the strut, fractal, grey level co-occurrence matrix, and radio-morphometric indices in the panoramic radiograph.

Method

Panoramic radiographs from 2019 to 2022 were used to assess pre- and postmenopausal women's bone mineral density. A total of 272 panoramic radiographs, which exhibited clear visibility of the mental foramen on both sides without any blurring, motion artefacts, surgical errors, overlapping hyoid bone, or inferior mandibular cortex, were divided into two groups. Group A (136 premenopausal women) and Group B (136 postmenopausal women). It is a retrospective study that is non-interventional/observational in design. Strut features, fractal dimensions, a grey-level co-occurrence matrix, and radio morphometric indices were used to investigate bone texture in an image processing program. The mean difference between group variables was calculated using an independent sample t-test/unpaired t-test.

Results

Pre-menopausal women had a mean age of 38.83 ± 6.01 years, while postmenopausal women had a mean age of 68.26 ± 8.31 In the postmenopausal group Four regions of interest exhibited fractal dimensions with a P value of less than 0.01 and GLCM features including contrast (0.812), correlation (0.230), energy (0.215), and homogeneity (0.322). Strut features of the four regions showed that 15 of 19 characteristics were significantly different.

Conclusion

Orthopantomogram is useful in screening for osteoporosis. Strut, radio-morphometric indices, and fractal analysis can assess bone texture and quality. Future research incorporating artificial intelligence can revolutionize image analysis and support clinical decision-making.

Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

What is the current state of artificial intelligence applications in dentistry and orthodontics?

BACKGROUND

The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from.

TYPES OF STUDIES REVIEWED

Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies.

RESULTS

The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable.

PRACTICAL IMPLICATIONS

AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.

Radiomics in bone pathology of the jaws

OBJECTIVE

To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response.

METHODS

A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

RESULTS

Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results.

CONCLUSIONS

GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.

Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network

OBJECTIVES

This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural networks (CNNs): Alexnet, VGG-16, and GoogLeNet; the performances of the constructed systems were evaluated.

METHODS

One oral radiologist classified 1500 panoramic radiographs into three types. In C1, the endosteal margin of the cortex was smooth and sharp, whereas porosities were observed in C2 and C3. The risks of osteoporosis were higher in C2 and C3 than in C1; C3 had the highest risk. This information was included with the images as training data; three CNNs were transfer trained. Using each trained CNN, the diagnostic accuracy was assessed using panoramic radiographs and bone mineral density inspection findings in the lumbar spine and femoral neck of 100 additional patients.

RESULTS

All CNNs exhibited relatively good agreement with the oral radiologist's judgement (86.0%-90.7%). The predictive results of the three systems for osteoporosis of the lumbar spine showed sensitivities of 78.3%-82.6%, specificities of 71.4%-79.2%, and accuracies of 74.0%-79.0%. The predictive results for osteoporosis of the femoral neck showed sensitivities of 80.0%-86.7%, specificities of 67.1%-74.1%, and accuracies of 70.0%-75.0%.

CONCLUSIONS

The constructed systems were generally more accurate than the previously developed conventional system. The new systems may facilitate osteoporosis prediction and prevent subsequent fractures by encouraging patients with suspected osteoporosis to undergo further inspections (e.g., dual-energy X-ray absorptiometry) and treatment.

Comparison of five convolutional neural networks for predicting osteoporosis based on mandibular cortical index on panoramic radiographs

OBJECTIVES

The aim of the present study was to compare five convolutional neural networks for predicting osteoporosis based on mandibular cortical index (MCI) on panoramic radiographs.

METHODS

Panoramic radiographs of 744 female patients over 50 years of age were labeled as C1, C2, and C3 depending on the MCI. The data of the present study were reviewed in different categories including (C1, C2, C3), (C1, C2), (C1, C3), and (C1, (C2 +C3)) as two-class and three-class predictions. The data were separated randomly as 20% test data, and the remaining data were used for training and validation with fivefold cross-validation. AlexNET, GoogleNET, ResNET-50, SqueezeNET, and ShuffleNET deep-learning models were trained through the transfer learning method. The results were evaluated by performance criteria including accuracy, sensitivity, specificity, F1-score, AUC, and training duration. The Gradient-Weighted Class Activation Mapping (Grad-CAM) method was applied for visual interpretation of where deep-learning algorithms gather the feature from image regions.

RESULTS

The dataset (C1, C2, C3) has an accuracy rate of 81.14% with AlexNET; the dataset (C1, C2) has an accuracy rate of 88.94% with GoogleNET; the dataset (C1, C3) has an accuracy rate of 98.56% with AlexNET; and the dataset (C1,(C2+C3)) has an accuracy rate of 92.79% with GoogleNET.

CONCLUSION

The highest accuracy was obtained in the differentiation of C3 and C1 where osseous structure characteristics change significantly. Since the C2 score represent the intermediate stage (osteopenia), structural characteristics of the bone present behaviors closer to C1 and C3 scores. Therefore, the data set including the C2 score provided relatively lower accuracy results.

Current applications and development of artificial intelligence for digital dental radiography

In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.

Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs

Recent studies have shown that Dental Panoramic Radiograph (DPR) images have great potential for prescreening of osteoporosis given the high degree of correlation between the bone density and trabecular bone structure. Most of the research works in these area had used pretrained models for feature extraction and classification with good success. However, when the size of the data set is limited it becomes difficult to use these pretrained networks and gain high confidence scores. In this paper, we evaluated the diagnostic performance of deep convolutional neural networks (DCNN)based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists. With the available labelled dataset of 70 images, results were reproduced for the preliminary study model. Furthermore, the model performance was enhanced using different computer vision techniques. Specifically, the age meta data available for each patient was leveraged to obtain more accurate predictions. Lastly, we tried to leverage these images, ages and osteoporotic labels to create a neural network based regression model and predict the Bone Mineral Density (BMD) value for each patient. Experimental results showed that the proposed CAD system was in high accord with experienced oral and maxillofacial radiologists in detecting osteoporosis and achieved 87.86% accuracy.Clinical relevance- This paper presents a method to detect osteoporosis using DPR images and age data with multi-column DCNN and then leverage this data to predict Bone Mineral Density for each patient.

Use of fractal analysis in dental images for osteoporosis detection: a systematic review and meta-analysis

Fractal dimension (FD) calculated on oral radiographs has been proposed as a useful tool to screen for osteoporosis. This systematic review and meta-analysis firstly aimed at assessing the reliability of FD measures in distinguishing osteoporotic patients (OP) from healthy controls (HC), and secondly, to identify a standardized procedure of FD calculation in dental radiographs for the possible use as a surrogate measure of osteoporosis. A comprehensive search was conducted up to September 2020 using PubMed, Web of Science, and SCOPUS databases. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was followed. Meta-analysis was performed on FD values calculated for HC and OP. Overall, 293 articles were identified. After a three steps screening, 19 studies were included in the qualitative appraisal and 12 were considered for meta-analysis. The methodological quality of the retrieved studies was generally low. Most of the studies included used White and Rudolph and box counting to process the images and to calculate FD, respectively. Overall, 51% of the studies found a meaningful difference between HC and OP groups. Meta-analyses showed that to date, FD measures on dental radiographs are not able to distinguish the OP from HC group significantly. From the current evidence, the use of FD for the identification of OP is not reliable, and no clear conclusion can be drawn due to the heterogeneity of studies. The present review revealed the need for further studies and provided the fundamentals to design them in order to find a standardized procedure for FD calculation (regions for FD assessment; images processing technique; methods for FD measurement). More effort should be made to identify osteoporosis using dental images which are cheap and routinely taken during periodic dental examinations.

Trabecular and cortical mandibular bone investigation in familial adenomatous polyposis patients

Mandibular cortical and trabecular bone abnormalities in patients with familial adenomatous polyposis (FAP) were evaluated using dental panoramic radiographs (DPR) radiomorphometric indices and fractal dimension (FD). Sixty DPRs from 15 FAP patients and 45 healthy controls were evaluated. FAP group was composed of 33.3% females and 66.6% males, age_mean = 37.2 years (SD 15.79). The non-FAP group was paired by gender and sex. The parameters analyzed were: FD of the trabecular bone in four regions of interest (ROI), mandibular cortical index (MCI) and width (MCW). FD values were lower for the FAP group. Statistically significance differences were shown by ROI 2 and 3 anteriorly to the mental foramen bilaterally, p = 0.001, and p = 0.006. The ROI 1 and 4, at the mandibular angle trabeculae, indicated statistical significances on the right side (p = 0.036) and no differences on the left side (p = 0.091). There was no significant difference in MCI and MCW when the groups were compared, MCW (L) p = 0.247, and MCW (R) p = 0.070. Fractal values of FAP patients' mandibular trabecular bone were lower than healthy controls. The radiomorphometric indices MCI and MCW were not useful for analyzing the cortical bone pattern. Therefore, FD is a promising tool for detection of abnormal bone structure in DPRs and for supporting the appropriate referral of FAP patients.

Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients

Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.

The Concept of Stress Shielding in Nonvascularized Bone Grafts of the Mandible-A Review of 2 Cases

Two cases are shown to demonstrate stress shielding in nonvascularized bone grafts to the mandible to reconstruct segmental defects, where rigid fixation is used. The effects are reversed on removal of the rigid fixation.

Detection of Trabecular Landmarks for Osteoporosis Prescreening in Dental Panoramic Radiographs

Dental panoramic radiography (DPR) images have recently attracted increasing attention in osteoporosis analysis because of their inner correlation. Many approaches leverage machine learning techniques (e.g., deep convolutional neural networks (CNNs)) to study DPR images of a patient to provide initial analysis of osteoporosis, which demonstrates promising results and significantly reduces financial cost. However, these methods heavily rely on the trabecula landmarks of DPR images that requires a large amount of manual annotations by dentist, and thus are limited in practical application. Addressing this issue, we propose to automatically detect trabecular landmarks in DPR images. In specific, we first apply CNNs-based detector for trabecular landmark detection and analyze its limitations. Using CNNs-based detection as a baseline, we then introduce a statistic shape model (SSM) for trabecular landmark detection by taking advantage of spatial distribution prior of trabecular landmarks in DPR images and their structural relations. In experiment on 108 images, our solution outperforms CNNs-based detector. Moreover, compared to CNN-based detectors, our method avoids the needs of vast training samples, which is more practical in application.

Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.

A comprehensive study on feature types for osteoporosis classification in dental panoramic radiographs

BACKGROUND AND OBJECTIVE

Osteoporosis is a disease characterized by a decrease in bone density. It is often associated with fractures and severe pain. Previous studies have shown a high correlation between the density of the bone in the hip and in the mandibular bone in the jaw. This suggests that dental radiographs might be useful for detecting osteoporosis. Use of dental radiographs for this purpose would simplify early detection of osteoporosis. However, dental radiographs are not normally examined by radiologists. This paper explores the use of 13 different feature extractors for detection of reduced bone density in dental radiographs.

METHODS

The computed feature vectors are then processed with a Self-Organizing Map and Learning Vector Quantization as well as Support Vector Machines to produce a set of 26 predictive models.

RESULTS

The results show that the models based on Self-Organizing Map and Learning Vector Quantization using Gabor Filter, Edge Orientation Histogram, Haar Wavelet, and Steerable Filter feature extractors outperform the rest of the 22 models in detecting osteoporosis. The proposed Gabor-based algorithm achieved an accuracy of 92.6%, a sensitivity of 97.1%, and a specificity of 86.4%.

CONCLUSIONS

The oriented edges and textures in the upper and lower jaw regions are useful for distinguishing normal patients from patients with osteoporosis.

Automatic computation of mandibular indices in dental panoramic radiographs for early osteoporosis detection

AIM

A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular Index, Mandibular Ratio, Mandibular Cortical Index) in order to detect the thinning and deterioration of the mandibular bone. Data can be stored for posterior massive analysis.

METHODS

Panoramic radiographies are intrinsically complex, including: artificial structures, unclear limits in bony structures, jawbones with irregular curvatures and intensity levels, irregular shapes and borders of the mental foramen, irregular teeth alignments or missing dental pieces. An intelligent sequence of linked imaging segmentation processes is proposed to cope with the above situations towards the design of the automatic segmentation, making the following contributions: (i) Fuzzy K-means classification for identifying artificial structures; (ii) adjust a tangent line to the lower border of the lower jawbone (lower cortex), based on texture analysis, grey scale dilation, binarization and labelling; (iii) identification of the mental foramen region and its centre, based on multi-thresholding, binarization, morphological operations and labelling; (iv) tracing a perpendicular line to the tangent passing through the centre of the mental foramen region and two parallel lines to the tangent, passing through borders on the mental foramen intersected by the perpendicular; (v) following the perpendicular line, a sweep is made moving up the tangent for detecting accumulation of binary points after applying adaptive filtering; (vi) detection of the lower mandible alveolar crest line based on the identification of inter-teeth gaps by saliency and interest points feature description.

RESULTS

The performance of the proposed approach was quantitatively compared against the criteria of expert dentists, verifying also its validity with statistical studies based on the analysis of deterioration of bone structures with different levels of osteoporosis. All indices are computed inside two regions of interest, which tolerate flexibility in sizes and locations, making this process robust enough.

CONCLUSIONS

The proposed approach provides an automatic procedure able to process with efficiency and reliability panoramic X-Ray images for early osteoporosis detection.

Use of fractal analysis in dental images: a systematic review

OBJECTIVES

This study reviewed the use of fractal analysis (FA) in dental images.

METHODS

A search was performed using PubMed, MEDLINE, LILACS, Web of Science and SCOPUS databases. The inclusion criteria were human studies in the English language, with no date restriction.

RESULTS

78 articles were found in which FA was applied to panoramic radiographs (34), periapical radiographs (21), bitewing radiographs (4), cephalometric radiograph (1), cone beam CT (15), micro-CT (3), sialography (2), and ultrasound (2). Low bone mineral density (21) and systemic or local diseases (22) around the bone of dental implants were the main subjects of the study of FA. Various sizes and sites of the regions of interest were used to evaluate the bone structure. Different ways were used to treat the image and to calculate FA. FA of 43 articles showed significant differences in the comparison of groups, mainly between healthy and sick patients.

CONCLUSIONS

FA in Dentistry has been widely applied to the study of images. Panoramic and periapical radiographs were those most frequently used. The Image J software and the box-counting method were extensively adopted in the studies reviewed herein. Further studies are encouraged to improve clarification of the parameters that directly influence FA.

The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review

OBJECTIVES

To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR).

METHODS

Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included.

RESULTS

The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms.

CONCLUSION

The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.

Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion

Considering the poor medical conditions in some regions of China, this paper attempts to develop a simple and easy way to extract and process the bone features of blurry medical images and improve the diagnosis accuracy of osteoporosis as much as possible. After reviewing the previous studies on osteoporosis, especially those focusing on texture analysis, a convexity optimization model was proposed based on intra-class dispersion, which combines texture features and shape features. Experimental results show that the proposed model boasts a larger application scope than Lasso, a popular feature selection method that only supports generalized linear models. The research findings ensure the accuracy of osteoporosis diagnosis and enjoy good potentials for clinical application.

Use of Texture Feature Maps for the Refinement of Information Derived from Digital Intraoral Radiographs of Lytic and Sclerotic Lesions

The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions.

Mandibular Trabecular Bone Analysis Using Local Binary Pattern for Osteoporosis Diagnosis

BACKGROUND

Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Since Panoramic image is a feasible and relatively routine imaging technique in dentistry; it could provide an opportunistic chance for screening osteoporosis. In this regard, numerous panoramic derived indices have been developed and suggested for osteoporosis screening. Jaw trabecular pattern is one of the main bone strength factors and trabecular bone pattern assessment is important factor in bone quality analysis. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs.

OBJECTIVE

The purpose of this study was to evaluate the relationship between Jaw trabecular pattern in panoramic image and osteoporosis based on image texture analyzing using local binary pattern.

MATERIAL AND METHODS

An experiment is evaluated in this paper based on a real hand-captured database of panoramic radiograph images from osteoporosis and non-osteoporosis person in Namazi Hospital, Shiraz, Iran .An approach is proposed for osteoporosis diagnosis consisting of two steps. First, modified version of local binary patterns is used to extract discriminative features from jaw panoramic radiograph images. Then, classification is done using different classifiers.

RESULTS

Comparative results show that the proposed approach provides classification accuracy about 99.6%, which is higher than many state-of-the-art methods.

CONCLUSION

High classification accuracy, low computational complexity, multi-resolution and rotation invariant are among advantages of our proposed approach.

Caries detection enhancement using texture feature maps of intraoral radiographs

Objectives

Dental caries are caused by tooth demineralization due to bacterial plaque formation. However, the resulting lesions are often discrete and thus barely recognizable in intraoral radiography images. Therefore, more advanced detection techniques are in great demand among dentists and radiographers. This study was performed to evaluate the performance of texture feature maps in the recognition of discrete demineralization related to caries plaque formation.

Methods

Digital intraoral radiology image analysis protocols incorporating first-order features (FOF), co-occurrence matrices, gray tone difference matrices, run-length matrices (RLM), local binary patterns (LBP), and k-means clustering (CLU) were used to transform the digital intraoral radiology images of 10 patients with confirmed caries, which were retrospectively reviewed in a dental clinic. The performance of the resulting texture feature maps was compared with that of radiographic images by radiologists and dental specialists.

Results

Significantly improved detection of caries spots was achieved by employing the CLU and FOF texture feature maps. The caries-affected area with sharp margins was well defined using the CLU approach. A pseudo-three-dimensional effect was observed in outlining the demineralization zones inside the cavity with the FOF 5 protocol. In contrast, the LBP and RLM techniques produced less satisfactory results with unsharp edges and less detailed depiction of the lesions.

Conclusions

This study illustrated the applicability of texture feature maps to the recognition of demineralized spots on the tooth surface debilitated by caries and identified the best performing techniques.

Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study

OBJECTIVES

To evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists.

METHODS

Oral and maxillofacial radiologists with >10 years of experience reviewed the panoramic radiographs of 1268 females {mean [± standard deviation (SD)] age: 52.5 ± 22.3 years} and made a diagnosis of osteoporosis when cortical erosion of the mandibular inferior cortex was observed. Among the females, 635 had no osteoporosis [mean (± SD) age: 32.8 ± SD 12.1 years] and 633 had osteoporosis (72.2 ± 8.5 years). All panoramic radiographs were analysed using three CAD systems, single-column DCNN (SC-DCNN), single-column with data augmentation DCNN (SC-DCNN Augment) and multicolumn DCNN (MC-DCNN). Among the radiographs, 200 panoramic radiographs [mean (± SD) patient age: 63.9 ± 10.7 years] were used for testing the performance of the DCNN in detecting osteoporosis in this study. The diagnostic performance of the DCNN-based CAD system was assessed by receiver operating characteristic (ROC) analysis.

RESULTS

The area under the curve (AUC) values obtained using SC-DCNN, SC-DCNN (Augment) and MC-DCNN were 0.9763, 0.9991 and 0.9987, respectively.

CONCLUSIONS

The DCNN-based CAD system showed high agreement with experienced oral and maxillofacial radiologists in detecting osteoporosis. A DCNN-based CAD system could provide information to dentists for the early detection of osteoporosis, and asymptomatic patients with osteoporosis can then be referred to the appropriate medical professionals.

Current and Emerging Diagnostic Imaging-Based Techniques for Assessment of Osteoporosis and Fracture Risk

Osteoporosis is a metabolic bone disorder characterized by low bone mass, degradation of bone microarchitecture, and susceptibility to fracture. It is a growing major health concern across the world, especially in the elderly population. Osteoporosis can cause hip or spinal fractures that may lead to high morbidity and socio-economic burden. Therefore, there is a need for early diagnosis of osteoporosis and prediction of fragility fracture risk. In this review, state of the art and recent advances in imaging techniques for diagnosis of osteoporosis and fracture risk assessment have been explored. Segmentation methods used to segment the regions of interest and texture analysis methods used for classification of healthy and osteoporotic subjects are also presented. Furthermore, challenges posed by the current diagnostic tools have been studied and feasible solutions to circumvent the limitations are discussed. Early diagnosis of osteoporosis and prediction of fracture risk require the development of highly precise and accurate low-cost diagnostic techniques that would help the elderly population in low economies.

Detecting the earliest radiological signs of bisphosphonate-related osteonecrosis

Introduction Oral bisphosphonates are the most commonly prescribed anti-resorptive drugs used in the treatment of osteoporosis, but osteonecrosis of the jaw is a serious complication. The early diagnosis of this destructive side effect is crucial in preventing excessive bone loss, pain and infection.Objective To aid dental practitioners in the early identification of bisphosphonate-related osteonecrosis of the jaw.Method A scoping review was carried out.Data sources We searched MEDLINE via OVID, EMBASE via OVID, Dentistry and Oral Sciences Source (DOSS), Proquest Dissertation and Theses Search, to identify references that described clinical and radiological findings in medication-related osteonecrosis of the jaw (MRONJ).Data selection Nineteen references mentioned the earliest radiological changes in MRONJ with a description of the observations and were included in the analysis.Data synthesis The radiographic signs included osteosclerosis/lysis, widening of the periodontal ligament and thickening of the lamina dura and cortex. To assess the quality of original data on which recommendations had been made, these 19 studies were subjected to a quality appraisal.Conclusion Using bone exposure as a criterion for diagnosis of MRONJ, leads to delayed diagnosis and a poor response to treatment. In those patients at risk of bone exposure with MRONJ, insufficient information is present in the literature to allow the general dental practitioner to reliably identify the radiographic features indicating imminent bone exposure. A well-designed prospective study is needed.

Characterization of dental pathologies using digital panoramic X-ray images based on texture analysis

Dental caries and the cysts of jaws are frequently occurring pathologies encountered in a dental practice. Imaging of these dental anomalies is done with radiographic examination. Panoramic radiography/ Orthopantomography (OPG) is a common modality to screen patients with an advantage of ease of imaging and reduced exposure to patients. The panoramic images obtained with this equipment are exploited by noise embedded during its acquisition making the detection of this dental caries difficult. Detection and characterization of dental caries and various other maxilla-facial pathologies can be achieved by the application of computer aided image processing algorithms applied on dental panoramic images. This paper presents two distinct image processing algorithms for detection of dental anomalies. The first part of this paper presents a novel approach for detection of dental caries using hybridized negative transformation. The second part of paper presents, statistical texture analysis for the dental images containing cysts along with dental caries. The texture analysis is used when the objects to be segmented based on texture content rather than intensities. The texture of panoramic image is characterized by Gray Level Co-occurrence Matrix (GLCM). The texture features obtained from the GLCM are energy, entropy, homogeneity, contrast and correlation. These texture features can be used to find texture boundaries to obtain segmentation about the region of cysts. Results obtained by both the methods were satisfactory correlating with the diagnosis made by the maxillofacial radiologists.

Strut analysis for osteoporosis detection model using dental panoramic radiography

OBJECTIVES

The aim of this study was to identify variables that can be used for osteoporosis detection using strut analysis, fractal dimension (FD) and the gray level co-occurrence matrix (GLCM) using multiple regions of interest and to develop an osteoporosis detection model based on panoramic radiography.

METHODS

A total of 454 panoramic radiographs from oral examinations in our dental hospital from 2012 to 2015 were randomly selected, equally distributed among osteoporotic and non-osteoporotic patients (n = 227 in each group). The radiographs were classified by bone mineral density (T-score). After 3 marrow regions and the endosteal margin area were selected, strut features, FD and GLCM were analysed using a customized image processing program. Image upsampling was used to obtain the optimal binarization for calculating strut features and FD. The independent-samples t-test was used to assess statistical differences between the 2 groups. A decision tree and support vector machine were used to create and verify an osteoporosis detection model.

RESULTS

The endosteal margin area showed statistically significant differences in FD, GLCM and strut variables between the osteoporotic and non-osteoporotic patients, whereas the medullary portions showed few distinguishing features. The sensitivity, specificity, and accuracy of the strut variables in the endosteal margin area were 97.1%, 95.7 and 96.25 using the decision tree and 97.2%, 97.1 and 96.9% using support vector machine, and these were the best results obtained among the 3 methods. Strut variables with FD and/or GLCM did not increase the diagnostic accuracy.

CONCLUSION

The analysis of strut features in the endosteal margin area showed potential for the development of an osteoporosis detection model based on panoramic radiography.

Model-Based Orthodontic Assessments for Dental Panoramic Radiographs

For better treatment outcomes, dentists usually use a set of parameters for orthodontic evaluation. In this study, a new method is proposed to assist dentists in obtaining reliable assessment of these parameters. The proposed method is based on dental panoramic radiographs and can be divided into four stages: image preprocessing, model training, tooth segmentation, and assessment of orthodontic parameters. The image is first normalized and enhanced. Then, the model training stage consists of shape and image model training, energy function training, and weight training. Next, we automatically segment the tooth contours in an energy-minimized manner. Finally, the automatic assessment of orthodontic parameters is carried out. The experimental results show that the average of absolute distance, the Dice similarity coefficient, and the average qualitative score ranged between 4.17 and 6.03, 0.87 and 0.90, as well as 2.58 and 3.12, respectively. The orthodontic assessment also is close to the evaluation of orthodontists. It has been shown that the proposed method can obtain accurate and consistent measurement in helping dentists to obtain an objective treatment evaluation.

Distributional Variations in the Quantitative Cortical and Trabecular Bone Radiographic Measurements of Mandible, between Male and Female Populations of Korea, and its Utilization

It is important to investigate the irregularities in aging-associated changes in bone, between men and women for bone strength and osteoporosis. The purpose of this study was to characterize the changes and associations of mandibular cortical and trabecular bone measures of men and women based on age and to the evaluation of cortical shape categories, in a large Korean population. Panoramic radiographs of 1047 subjects (603 women and 444 men) aged between 15 to 90 years were used. Mandibular cortical width (MCW), mandibular cortical index (MCI), and fractal dimensions (FD) of the molar, premolar, and anterior regions of the mandibular trabecular bone were measured. Study subjects were grouped into six 10-years age groups. A local linear regression smoothing with bootstrap resampling for robust fitting of data was used to estimate the relationship between radiographic mandibular variables and age groups as well as genders. The mean age of women (49.56 ± 19.5 years) was significantly higher than that of men (45.57 ± 19.6 years). The MCW of men and women (3.17mm and 2.91mm, respectively, p < 0.0001) was strongly associated with age and MCI. Indeed, trabecular measures also correlated with age in men (r > −0.140, p = 0.003), though not as strongly as in women (r > −0.210, p < 0.0001). In men aged over 55 years, only MCW was significantly associated (r = −0.412, p < 0.0001). Furthermore, by comparison of mandibular variables from different age groups and MCI categories, the results suggest that MCW was detected to be strongly associated in both men and women for the detection of bone strength and osteoporosis. The FD measures revealed relatively higher association with age among women than men, but not as strong as MCW.

IDIOS: An innovative index for evaluating dental imaging-based osteoporosis screening indices

Purpose

The goal of this study was to develop a new index as an objective reference for evaluating current and newly developed indices used for osteoporosis screening based on dental images. Its name; IDIOS, stands for Index of Dental-imaging Indices of Osteoporosis Screening.

Materials and Methods

A comprehensive PubMed search was conducted to retrieve studies on dental imaging-based indices for osteoporosis screening. The results of the eligible studies, along with other relevant criteria, were used to develop IDIOS, which has scores ranging from 0 (0%) to 15 (100%). The indices presented in the studies we included were then evaluated using IDIOS.

Results

The 104 studies that were included utilized 24, 4, and 9 indices derived from panoramic, periapical, and computed tomographic/cone-beam computed tomographic techniques, respectively. The IDIOS scores for these indices ranged from 0 (0%) to 11.75 (78.32%).

Conclusion

IDIOS is a valuable reference index that facilitates the evaluation of other dental imaging-based osteoporosis screening indices. Furthermore, IDIOS can be utilized to evaluate the accuracy of newly developed indices.

Quantitative assessment of mandibular cortical erosion on dental panoramic radiographs for screening osteoporosis

PURPOSE

Studies reported that the mandibular cortical width (MCW) measured on dental panoramic radiographs (DPRs) was significantly correlated with bone mineral density. However, MCW is not a perfect index by itself, and studies suggest the added utility of mandibular cortical index (MCI). In this study, we propose a method for computerized estimation of mandibular cortical degree (MCD), a new continuous measure of MCI, for osteoporotic risk assessment.

METHODS

The mandibular contour was automatically segmented using an active contour model. The regions of interest near mental foramen were extracted for MCW and MCD determination. The MCW was measured on the basis of residue-line detection results and pixel profiles. Image features including texture features based on gray-level co-occurrence matrices were determined. The MCD were estimated using support vector regression (SVR). The SVR was trained using previously collected 99 DPRs, including 26 osteoporotic cases, by a computed radiography system. The proposed scheme was tested using 99 DPRs obtained by a photon-counting system with data of bone mineral density at distal forearm. The number of osteoporotic, osteopenic, and control cases were 12, 18, and 69 cases, respectively. The subjective MCD by a dental radiologist was employed for training and evaluation.

RESULTS

The correlation coefficients with the subjective MCD were -0.549 for MCW alone, 0.609 for the MCD by the features without MCW, and 0.617 for the MCD by the features and MCW. The correlation coefficients with the BMD were 0.619, -0.608, and -0.670, respectively. The areas under the receiver operating characteristic curves for detecting osteoporotic cases were 0.830, 0.884, and 0.901, respectively, whereas those for detecting high-risk cases were 0.835, 0.833, and 0.880, respectively.

CONCLUSIONS

In conclusion, our scheme may have a potential to identify asymptomatic osteoporotic and osteopenic patients through dental examinations.

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches

OBJECTIVES

This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis.

METHODS

The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers.

RESULTS

Proposed hybrid GSF classifier in identifying low BMD or osteoporosis at the lumbar spine and femoral neck BMD was evaluated. The sensitivity, specificity and accuracy of the hybrid GSF with optimized MF and RS in identifying females with a low BMD were 95.3%, 94.7% and 96.01%, respectively, at the lumbar spine and 99.1%, 98.4% and 98.9%, respectively, at the femoral neck BMD. The diagnostic performance of the proposed system with femoral neck BMD was 0.986 with a confidence interval of 0.942-0.998. The highest mean accuracy using 5-FCV was 97.9% with femoral neck BMD.

CONCLUSIONS

The combination of high accuracy along with its interpretation ability makes this proposed automatic system using hybrid GSF classifier capable of identifying a large proportion of undetected low BMD or osteoporosis at its early stage.

Detecting low bone mineral density from dental radiographs: a mini-review

Over a number of years researchers have reported associations between osteoporosis or low bone mineral density and signs that can be detected on dental radiographs, particularly in the width of the inferior mandibular cortex and the texture of the trabecular bone. As patients visit the dentist more regularly than they visit their doctor, there is the possibility that such signs could be used as a means of identifying individuals at risk of developing osteoporosis or suffering from consequent fracture. This paper reviews the historical background behind this research and the current status, including recent developments in automation of measurement using computer image analysis.

Panoramic measures for oral bone mass in detecting osteoporosis: a systematic review and meta-analysis

Different quantitative and qualitative indices calculated on oral panoramic radiographs have been proposed as useful tools to screen for reduced skeletal bone mineral density (BMD). Our aim was to systematically review the literature on linear and qualitative panoramic measures and to assess the accuracy of these indices by performing a meta-analysis of their sensitivity and specificity. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was followed. Fifty studies were included in the qualitative appraisal and 19 were considered for meta-analysis. The methodological quality of the retrieved studies, assessed with the QUADAS-2 tool, was on average low. Three indices were reported by most of the studies: mandibular cortical width, panoramic mandibular index, and the Klemetti index. Mandibular cortical width presented with a better accuracy in excluding osteopenia/osteoporosis (specificity), since patients with a cortical width more than 4 mm had a normal BMD in 90% of the cases. Almost all studies used a cutoff of 0.3 for the panoramic mandibular index, resulting in an estimated sensitivity and specificity in detecting reduced BMD, respectively, of 0.723 (SE 0.160; 95% confidence interval [CI], 0.352-0.926) and 0.733 (SE 0.066; 95% CI, 0.587-0.841). The presence of any kind of mandibular cortical erosion gave an estimated sensitivity and specificity in detecting reduced BMD, respectively, of 0.789 (SE 0.031; 95% CI, 0.721-0.843) and 0.562 (SE 0.047; 95% CI, 0.47-0.651) and a sensitivity and specificity in detecting osteoporosis, respectively, of 0.806 (SE 0.105; 95% CI, 0.528-0.9200) and 0.643 (SE 0.109; 95% CI, 0.417-0.820). The mandibular cortical width, panoramic mandibular index, and Klemetti index are overall useful tools that potentially could be used by dentists to screen for low BMD. Their limitations are mainly related to the experience/agreement between different operators and the different image quality and magnification of the panoramic radiographs.