Automated measurement of mandibular cortical width on dental panoramic radiographs

Evaluation of dental manifestations in X-linked hypophosphatemia using orthopantomography

BACKGROUND

X-linked hypophosphatemia (XLH) is the most common inherited form of rickets. The presence of sequence variations in the phosphate regulating endopeptidase homolog X-linked (PHEX) gene is associated with increased production of fibroblast growth factor 23 (FGF23). This results in renal phosphate wasting and impaired skeletal mineralization. Spontaneous dental abscesses, caused by endodontic infections resulting from hypomineralization of dentin, are a known dental complication of XLH. There is no objective method to evaluate the severity of dentin dysplasia. The purpose of this study was to develop a quantitative method to evaluate dentin dysplasia using orthopantomography that would allow the values in patients with XLH to be compared with the values in healthy participants of the same age.

METHODS

The severity of dentin dysplasia was analyzed by measuring the pulp cavity area of the tooth using orthopantomographic images. The teeth analyzed were mandibular second primary molars and mandibular first permanent molars with complete root formation. Teeth with dental caries, restorations, or root resorption were excluded.

RESULTS

This retrospective observational study included a total of 200 images of healthy participants (aged 2-15 years) divided into five age groups and 42 images of 17 patients with XLH. There was a significant tendency for the pulp cavity area to decrease with increasing age in primary and permanent teeth. The pulp chambers of patients with XLH were larger than those of healthy participants in primary and permanent teeth.

CONCLUSION

We have established a method of using orthopantomography for quantitative assessment of dentin dysplasia in XLH from the primary dentition to the permanent dentition. Evaluating the severity of dentin hypomineralization by this method is useful in the diagnosis of the dental manifestations of XLH. Early diagnosis of XLH enables oral management and leads to prevention of dental abscesses.

Quantitative analysis of mandibular cortical morphology using artificial intelligence-based computer assisted diagnosis for panoramic radiography on underlying diseases and dental status in women over 20 years of age

Background/purpose

Recently, an artificial intelligence-based computer-assisted diagnosis (AI-CAD) for panoramic radiography was developed to scan the inferior margin of the mandible and automatically evaluate mandibular cortical morphology. The aim of this study was to analyze quantitatively the mandibular cortical morphology using the AI-CAD, especially focusing on underlying diseases and dental status in women over 20 years of age.

Materials and methods

419 patients in women over 20 years of age who underwent panoramic radiography were included in this study. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of the mandibular inferior cortex (MIC) and mandibular cortical index (MCI) automatically. Those were analyzed in relation to underlying diseases, such as diabetes, hypertension, dyslipidemia, rheumatism and osteoporosis, and dental status, such as the number of teeth present in the maxilla and mandible.

Results

The degree of deformation of MIC in women under 51 years of age (21-50 years; n = 229, 16.0 ± 12.7) was significantly lower than those of over 50 years of age (51-90 years; n = 190, 45.1 ± 23.0), and the MCI was a significant difference for the different age group. Regarding the degree of deformation of MIC and MCI in women over 50 years of age, osteoporosis and number of total teeth present in the maxilla and mandible were significant differences.

Conclusion

The results of this study indicated that the mandibular cortical morphology using the AI-CAD is significantly related to osteoporosis and dental status in women over 50 years of age.

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.

Deep learning and artificial intelligence in dental diagnostic imaging

The application of artificial intelligence (AI) based on deep learning in dental diagnostic imaging is increasing. Several popular deep learning tasks have been applied to dental diagnostic images. Classification tasks are used to classify images with and without positive abnormal findings or to evaluate the progress of a lesion based on imaging findings. Region (object) detection and segmentation tasks have been used for tooth identification in panoramic radiographs. This technique is useful for automatically creating a patient's dental chart. Deep learning methods can also be used for detecting and evaluating anatomical structures of interest from images. Furthermore, generative AI based on natural language processing can automatically create written reports from the findings of diagnostic imaging.

AI-based computer-aided diagnosis for panoramic radiographs: Quantitative analysis of mandibular cortical morphology in relation to age and gender

OBJECTIVE

This study aimed to investigate AI-based computer-aided diagnosis (AI-CAD) for panoramic radiographs, especially quantitative evaluation of mandibular cortical morphology in relation to age and gender.

METHODS

321 patients with jaw lesions who underwent panoramic radiography were prospectively included. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of mandibular inferior cortex and mandibular cortical index (MCI) automatically. Those were analyzed in relation to age and gender, such as younger (≦ 20 years), middle (21-60 years) and older group (≧ 61 years) in men and women.

RESULTS

The degree of deformation in older men (33.0 ± 18.5) was higher than those of middle (25.0 ± 15.3, p = 0.030) and younger (32.5 ± 16.9, p = 0.993), and those in older women (46.2 ± 22.5) was higher than those of middle (19.4 ± 16.5, p < 0.001) and younger (22.4 ± 14.5, p < 0.001). The MCI of women was a significant difference for aging (p < 0.001), although those of men was not significant difference for aging (p = 0.189).

CONCLUSION

The AI-CAD could be a useful tool for the quantitative analysis of mandibular cortical morphology.

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%).

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates.

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.

Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge

Panoramic dental X-ray imaging is an established method for the diagnosis of dental problems. However, the resolution of panoramic dental X-ray images is relatively low. Thus, early lesions are often overlooked. As the first step in the development of a computer-aided diagnosis scheme for panoramic dental X-ray images, we propose a computerized method for the segmentation of teeth using U-Net with a loss function weighted on the tooth edge. Our database consisted of 162 panoramic dental X-ray images. The training dataset consisted of 102 images, while the remaining 60 images were used as the test dataset. The loss function obtained by the cross entropy (CE) in the entire image is usually used in training U-Net. To improve the segmentation accuracy of the tooth edge, a loss function weighted on the tooth edge is proposed by adding the CE in the tooth edge region to the CE for the entire image. The mean Jaccard index and Dice index for U-Net with the loss function combining the CEs for the entire image and tooth edge were 0.864 and 0.927, respectively, which were significantly larger than those for U-Net with the CE for the entire image (0.802 and 0.890, p < 0.001) and U-Net with the CE for the tooth edge (0.826 and 0.905, p < 0.001). U-Net with the new loss function exhibited a higher segmentation accuracy of the tooth in panoramic dental X-ray images than that obtained by U-Net with the conventional loss function.

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.

Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data

OBJECTIVES

Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases.

METHODS

One hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies.

RESULTS

The tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types.

CONCLUSIONS

The proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.

Computer programme to assess mandibular cortex morphology in cases of medication-related osteonecrosis of the jaw with osteoporosis or bone metastases

Purpose

The purpose of this study was to evaluate the morphology of the mandibular cortex in cases of medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis or bone metastases using a computer programme.

Materials and Methods

Fifty-four patients with MRONJ (35 with osteoporosis and 19 with bone metastases) were examined using panoramic radiography. The morphology of the mandibular cortex was evaluated using a computer programme that scanned the mandibular inferior cortex and automatically assessed the mandibular cortical index (MCI) according to the thickness and roughness of the mandibular cortex, as follows: normal (class 1), mildly to moderately eroded (class 2), or severely eroded (class 3). The MCI classifications of MRONJ patients with osteoporosis or bone metastases were evaluated with the Pearson chi-square test. In these analyses, a 5% significance level was used.

Results

The MCI of MRONJ patients with osteoporosis (class 1: 6, class 2: 15, class 3: 14) tended to be higher than that of patients with bone metastases (class 1: 14, class 2: 5, class 3: 0) (P=0.000).

Conclusion

The use of a computer programme to assess mandibular cortex morphology may be an effective technique for the objective and quantitative evaluation of the MCI in MRONJ patients with osteoporosis or bone metastases.

Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

OBJECTIVES

The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.

METHODS

Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.

RESULTS

Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.

CONCLUSIONS

The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.

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.

Improvement of region of interest extraction and scanning method of computer-aided diagnosis system for osteoporosis using panoramic radiographs

OBJECTIVES

Patients undergoing osteoporosis treatment benefit greatly from early detection. We previously developed a computer-aided diagnosis (CAD) system to identify osteoporosis using panoramic radiographs. However, the region of interest (ROI) was relatively small, and the method to select suitable ROIs was labor-intensive. This study aimed to expand the ROI and perform semi-automatized extraction of ROIs. The diagnostic performance and operating time were also assessed.

METHODS

We used panoramic radiographs and skeletal bone mineral density data of 200 postmenopausal women. Using the reference point that we defined by averaging 100 panoramic images as the lower mandibular border under the mental foramen, a 400 × 100-pixel ROI was automatically extracted and divided into four 100 × 100-pixel blocks. Valid blocks were analyzed using program 1, which examined each block separately, and program 2, which divided the blocks into smaller segments and performed scans/analyses across blocks. Diagnostic performance was evaluated using another set of 100 panoramic images.

RESULTS

Most ROIs (97.0%) were correctly extracted. The operation time decreased to 51.4% for program 1 and to 69.3% for program 2. The sensitivity, specificity, and accuracy for identifying osteoporosis were 84.0, 68.0, and 72.0% for program 1 and 92.0, 62.7, and 70.0% for program 2, respectively. Compared with the previous conventional system, program 2 recorded a slightly higher sensitivity, although it occasionally also elicited false positives.

CONCLUSIONS

Patients at risk for osteoporosis can be identified more rapidly using this new CAD system, which may contribute to earlier detection and intervention and improved medical care.

Use of cone beam computed tomography in identifying postmenopausal women with osteoporosis

The aim of this study is to correlate radiometric indices from cone beam computed tomography (CBCT) images and bone mineral density (BMD) in postmenopausal women. Quantitative CBCT indices can be used to screen for women with low BMD.

PURPOSE

Osteoporosis is a disease characterized by the deterioration of bone tissue and the consequent decrease in BMD and increase in bone fragility. Several studies have been performed to assess radiometric indices in panoramic images as low-BMD predictors. The aim of this study is to correlate radiometric indices from CBCT images and BMD in postmenopausal women.

METHODS

Sixty postmenopausal women with indications for dental implants and CBCT evaluation were selected. Dual-energy X-ray absorptiometry (DXA) was performed, and the patients were divided into normal, osteopenia, and osteoporosis groups, according to the World Health Organization (WHO) criteria. Cross-sectional images were used to evaluate the computed tomography mandibular index (CTMI), the computed tomography index (inferior) (CTI (I)) and computed tomography index (superior) (CTI (S)). Student's t test was used to compare the differences between the indices of the groups' intraclass correlation coefficient (ICC).

RESULTS

Statistical analysis showed a high degree of interobserver and intraobserver agreement for all measurements (ICC > 0.80). The mean values of CTMI, CTI (S), and CTI (I) were lower in the osteoporosis group than in osteopenia and normal patients (p < 0.05). In comparing normal patients and women with osteopenia, there was no statistically significant difference in the mean value of CTI (I) (p = 0.075).

CONCLUSIONS

Quantitative CBCT indices may help dentists to screen for women with low spinal and femoral bone mineral density so that they can refer postmenopausal women for bone densitometry.

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.

Osteoporosis and jawbones in women

Osteoporosis is a major health problem affecting one in three women over the age of 50 and may not be detected until fractures occur. Since osteoporotic fractures are a health burden worldwide, identifying subjects with a high risk of osteoporosis and preventing osteoporosis-related mortality and morbidity are a very important health strategy. Women show an estrogen-related bone loss starting at menopause, predominantly occurring in trabecular bone. Diagnosis of osteoporosis is usually based on the bone mineral density measurement, but this is not a practical and economical technique for early detection. Therefore, investigators are interested in the possibility of detecting osteoporosis from the panoramic radiographs. Mandibular cortical bone undergoes resorptive activity in osteoporotic patients, leading to a decreased thickness and more porous inferior border. Therefore, studies have demonstrated the usefulness of cortical width and shape, determined from panoramic radiographs, in identifying elderly individuals with undetected osteoporosis, especially postmenopausal women. In conclusion, postmenopausal women with C3 category, Mental Index (MI) <3 mm, and panoramic mandibular index (PMI) <0.3 may be considered for further osteoporosis investigation.

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.

Prediction of low bone mass using a combinational approach of cortical and trabecular bone measures from dental panoramic radiographs

The aim of this study is to extract cortical and trabecular features of the mandible and to develop a novel combinational model of mandibular cortical thickness, trabecular bone area and age in order to predict low bone mineral density or osteoporosis from a dental panoramic radiograph. The study involved 64 south Indian women (age = 52.5 ± 12.7 years) categorised into two groups (normal and low bone mineral density) based on total femur bone mineral density. The dental panoramic radiographs were obtained by a digital scanner, and measurement of total bone mineral density at the right femur was performed by a dual-energy X-ray absorptiometry scanner. The mandibular cortical thickness and panoramic mandibular index were measured bilaterally, and the mean values were considered. The region of interest of 128 × 128 pixels around the mental foramen region was manually cropped and subjected to pre-processing, normalisation and average threshold-based segmentation to determine trabecular bone area. Multiple linear regression analyses of cortical and trabecular measures along with age were performed to develop a combinational model to classify subjects as normal and low bone mineral density. The proposed approach demonstrated strong correlation (r = 0.76; p < 0.01) against the total bone mineral density and resulted in accuracy, sensitivity and positive predictive values of 0.84, 0.92 and 0.85, respectively; the receiver operating characteristic outcomes disclosed that the area under the curve was 0.89.Our results suggest that the proposed combinational model could be useful to diagnose subjects with low bone mineral density.

[Improvements to an automated method for detecting carotid artery calcifications by adopting a positional feature and feature selection]

The purpose of this study was to improve an automated scheme for detecting carotid artery calcification (CAC) in dental panoramic radiographs (DPRs). Using 100 DPRs, the sensitivity of CAC detection employing our previous method was 90.0% with 5.0 false positives (FPs) per image. This study describes two enhancements. One is the adoption of a new feature for the position of CACs in addition to previous features. The other is feature selection employing the support vector machine using all combinations. Five of 12 features were selected. Using our proposed method, the average sensitivity for the same database proved to be 90.0%, with only 2.5 FPs per image. These results indicate the potential effectiveness of the new positional feature and feature selection.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis

Purpose

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis.

Materials and Methods

We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion.

Results

The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck.

Conclusion

Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

Study of edge detection task in dental panoramic radiographs

The purpose of this study is (1) to introduce a new approach for edge detection in orthopantograms (OPGs) and an improved automatic parameter selector for common edge detectors, (2) to present a comparison between our novel approach with common edge detectors and (3) to provide faster outputs without compromising quality. A new approach for edge detection based on statistical measures was introduced: (1) a set of N edge detection results is calculated from a given input image and a selected type of edge detector, (2) N correspondence maps are constructed from N edge detection results, (3) probabilities and average probabilities are computed, (4) an overall correspondence is evaluated for each correspondence map and (5) the correspondence map providing the best overall correspondence is taken as the result of edge detection procedure. A comparison with common edge detectors (the Roberts, Prewitt, Sobel, Laplacian of the Gaussian and Canny methods) with various parameter settings (304 combinations for each test image) was carried out. The methods were assessed objectively [edge mismatch error (EME), modified Hausdorff distance (MHD) and principal component analysis] and subjectively by experts in dentistry and based on time demands. The suitability of the new approach for edge detection in OPGs was confirmed by experts. The current conventional methods in edge detection in OPGs are inadequate (none of the tested methods reach an EME value or MHD value below 0.1). Our proposed approach for edge detection shows promising potential for its implementation in clinical dentistry. It enhances the accuracy of OPG interpretation and advances diagnosis and treatment planning.