Dental Periapical Radiographs Research Articles

Fractal Analysis of Trabecular Alveolar Bone with Intrabony and Furcation Defects Using Periapical Dental Radiographs

Objective: Fractal analysis (FA) is a non-invasive method that quantitatively measures complex patterned geometric structures present throughout the image. Trabecular morphology of the alveolar bone and the changes occurring in the trabeculae in case of periodontitis can be detected with this method. To examine the periodontal defects in human skull bones using the FA, to compare them with healthy alveolar bone regions. Methods: Furcation and intrabony defects were artificially created in the mandible alveolar bones (n:24). Periapical X-ray images of alveolar bone regions containing teeth with defects were taken using the parallel technique. Fractal analysis was performed by box-counting method using Image J software on images from areas containing healthy and defective trabecular bone. Results: No statistically significant difference was found between the fractal values of healthy tissue and bifurcation defetcs and between the healthy tissue and intrabony defects (p>0.05). Conclusion: Many factors may have affected the outcomes; patient selection, imaging methods, sample size, Region of interest (ROI) selection-location and size, individual and anatomical variations. These variables need to be standardized as much as possible and the limitations of the method need to be improved.

Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph

Background/PurposeThe preciseness of detecting periodontal bone loss is examiners dependent, and this leads to low reliability. The need for automated assistance systems on dental radiographic images has been increased. To the best of our knowledge, no studies have quantitatively and automatically staged periodontitis using dental periapical radiographs. The purpose of this study was to evaluate periodontal bone loss and periodontitis stage on dental periapical radiographs using deep convolutional neural networks (CNNs). Materials and methods336 periapical radiographic images (teeth: 390) between January 2017 and December 2019 were collected and de-identified. All periapical radiographic image datasets were divided into training dataset (n = 82, teeth: 123) and test dataset (n = 336, teeth: 390). For creating an optimal deep CNN algorithm model, the training datasets were directly used for the segmentation and individual tooth detection. To evaluate the diagnostic power, we calculated the degree of alveolar bone loss deviation between our proposed method and ground truth, the Pearson correlation coefficients (PCC), and the diagnostic accuracy of the proposed method in the test datasets. ResultsThe periodontal bone loss degree deviation between our proposed method and the ground truth drawn by the three periodontists was 6.5 %. In addition, the overall PCC value of our proposed system and the periodontists’ diagnoses was 0.828 (P < 0.01). The total diagnostic accuracy of our proposed method was 72.8 %. The diagnostic accuracy was highest for stage III (97.0 %). ConclusionThis tool helps with diagnosis and prevents omission, and this may be especially helpful for inexperienced younger doctors and doctors in underdeveloped countries. It could also dramatically reduce the workload of clinicians and timely access to periodontist care for people requiring advanced periodontal treatment.

Application of Kvaal’s Age Estimation Method in Maxillary Central Incisor: A CBCT Study

Background: Radiographic dental age estimation methods are viable both for living and deceased people. One such method is the indirect assessment of quantified secondary dentinal deposition through measurements of tooth and pulp. Kvaal, et al., developed a method for chronological age estimation based on pulp size using periapical dental radiographs. There is a need to test this method of age estimation in the Indian population on living individuals not requiring tooth extraction. The current study aimed to assess the applicability of Kvaal’s method in maxillary permanent central incisor using CBCT. Materials and Methods: The study included 185 CBCT images of the individuals, ranging in age from 14 to 64 years. CBCT images were evaluated for the maxillary central incisor and metric measurements were taken from which ratios were derived. Using the ratios, a linear regression equation was derived, from which the age of an individual was predicted. Result and Conclusion: The correlation between the individual ratio and the chronological age was calculated using the Pearson correlation coefficient. The age of the individual was predicted using a linear regression equation with a SEE ranging from 10.05 to 12.78 years. When the samples were divided into various age groups, the Standard Error of Estimate has drastically reduced. The radiographic pulpal morphometric analysis used in present study can be recommended to assess the age of an adult for forensic purposes.

Radiographic Examination before Dental Extraction from Dentists' Perspective.

It is generally agreed that radiographic examination is important before dental extraction. It provides information about the roots and the surrounding tissues. In terms of practice, it does not seem to be a universally implemented protocol regarding the use of dental radiology before dental extraction. Besides, the type of radiographic technique is not specified. Some references prefer periapical dental radiographs. Others prefer orthopantomography), or even cone beam computed tomography Delpachitra et al. (2021) [1]. In terms of the dental practice, it is not clear whether there is a universally adopted protocol regarding the use of dental radiographs before dental extraction. Aim of the study. To assess dental professionals' perspective toward radiographic examination before conventional dental extraction. A Google form questionnaire was circulated to different dental professionals using mainly ResearchGate, in addition to different social media platforms. One hundred and forty-five dentists participated in the questionnaire. The respondents were divided according to the country of current practice: national (Iraqi), regional (Middle Eastern), and international participants. Out of 144 respondents, 51.4% percent of the participants were international, while 40.3% were Iraqis, and 8.3% were from the Middle East. The need for dental radiography in all dental extraction procedures was reported in the majority of responses (n = 86). Only 11 dentists think there is no necessity for radiographic examination before conventional extraction. The chi-square test showed a highly significant relationship between the country of current practice and the need for X-ray examination for conventional dental extraction (P < 0.01). Seventy-six dentists prefer periapical radiographs. Thirty-five preferred orthopantomography. A highly significant relationship was found between the country of practice and the X-ray technique (P < 0.01). The study showed that there is no universally adopted protocol regarding the use of dental radiography before dental extraction. The country of practice appears to govern the dentists' decisions regarding the need for an X-ray and the type of radiography prior to dental extraction. Periapical radiographs for posterior teeth seem to be the preferable choice before dental extraction.

Automatic Feature Segmentation in Dental Periapical Radiographs.

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system.

Fractal analysis of dental periapical radiographs: A revised image processing method

To assess trabecular bone structure as calculated with fractal analysis by 2 binarization processes: White and Rudolph's original method (WR.o) and a revised version (WR.r). Fractal dimension (FD) values calculated with WR.r (FD.r) and a gray-scale-based method (FD.f) were also compared. FD, histogram parameters, and lacunarity were compared by dentate status, jaw location, and sex. Regions of interest from digital periapical radiographs were defined below the teeth roots and in the edentulous sites of 37 patients. Histograms were assessed for pixel values. Binarization was performed with WR.o and then with WR.r, in which the outliers were removed. FD was assessed using WR.r (FD.r) and (FD.f). Histograms were assessed to obtain pixel values. Lacunarity was calculated. WR.r revealed fewer trabeculae, branches, and junctions than WR.o (P < .0001). The majority of the mean differences between FD.r and FD.f were within the 95% CI. Dentate areas had greater mean gray levels than partially edentulous areas (P=.0027). FD.f was higher in the mandible (P=.01), but gray-level SD (P < .0001) and lacunarity (P=.02) were greater in the maxilla. FD.f and lacunarity were higher (P=.0005) and lower (P=.0014) in males, respectively. WR.r was effective in revealing skeletonized bone trabeculae by removing non-trabecular noise. FD.r and FD.f revealed good agreement. FD.f, histogram parameters, and lacunarity differed based on dentate status, jaw location, and sex.

AGE ESTIMATION IN INDIAN POPULATION BY KVAAL’S METHOD USING MAXILLARY CANINE

Introduction: Age estimation in adults is advantageous in forensic odontology. With increase in age, dental pulp volume gets reduced due to secondary dentine formation. Kvaal et al. formulated a method for chronological age estimation based on pulp size by periapical dental radiographs. Aim and objective: Testing the Kvaal’s method for chronological age estimation in Indian population using patients’ intraoral periapical radiographs of maxillary canine. Materials and methods: This study included total of 100 subjects out of which 48 were females and 52 were males of age group 21-70 years. The ratios A1, A2, A3, A4 were calculated – pulp to root length, pulp to tooth length, tooth to root length, pulp to root width at three different levels respectively to avoid any discrepancy in magnification and angulation ofx-ray. Measurements were done using Kvaal’s method and was analyzed for regression analysis and regression equations were obtained and checked for the results. Results: PCC indicated that among the ratiosA1, A2, A4 were less statistically significant compared toA3 which was more significant individually. Conclusion: Ratio A3 is a good indicator and thus linear regression was derived by this variable and all variables together as well. A1, A2, A4 were not good indicators individually for age estimation.

Age Estimation by Kvaal's Method Using Digital Panoramic Radiographs in the Saudi Population.

IntroductionIn forensic literature, estimating an individual's age has garnered a lot of attention. With increasing age, the size of the dental pulp cavity shrinks as a result of secondary dentin deposits. This could be used as a measure of age. Aside from morphological approaches, radiological approaches might be used to analyze this regression shift. Kvaal's method calculates the chronological age of individuals based on the age-pulp size relationship on periapical dental radiographs.PurposeThis study aims to use Kvaal's method to estimate the chronological age of patients using digital panoramic radiographs and verify the validity of regression equations proposed by Kvaal et al. in the Saudi population.Material and methodsA total of 74 digital orthopantomograms were randomly selected from Qassim University Dental Clinic in Saudi Arabia, ranging in age from 18 to 64 years (mean age 32 years). The radiographs were taken between 2018 and 2021 according to inclusion and exclusion criteria.ResultsWhen the Kvaal technique was applied to Saudi members, there was no statistically significant discrepancy between the estimated and chronological ages. The coefficient of determination R2 was highest when three mandibular teeth were evaluated together (0.752).ConclusionThe most accurate indicator for age assessment was "M" (mean worth, all things considered) and "W L" (contrast among "Width" and "Length").

Periapical Radiograph Texture Features for Osteoporosis Detection using Deep Convolutional Neural Network

Currently, research for osteoporosis examination using dental radiographic images is increasing rapidly. Many researchers have used various methods from subject data. It indicates that osteoporosis has become a widespread disease that should be studied more deeply. This study proposes a deep Convolutional Neural Network architecture as a texture feature of dental periapical radiograph for osteoporosis detection. The subject of this study is postmenopausal Javanese women aged over 40 and data measurement result of Bone Mineral Density. The proposed model is divided into stages: 1) stage image acquisition and RoI selection, 2) stage feature extraction and classification. Various experiments with the number of convolution layers (3 layers to 6 layers) and various input block sizes and other hyper parameters were used to get the best model. The best model is obtained when the input image size is greater than 100 and less than 150 and a five of convolution layer, as well as other hyper parameters, including epochs=100, dropout=0.5, learning rate=0.0001, batch size= 16 and loss function using Adam's optimization. Validation and testing accuracy achieved by the best model is 98.10%, and 92.50. The research shows that the bigger images provide additional information about trabecular patterns in normal, osteopenia and osteoporosis classes, so that the proposed method using deep convolutional neural network as textural feature of the periapical radiograph achieves a good performance for detection osteoporosis.

Dental disease detection on periapical radiographs based on deep convolutional neural networks.

It is with a great prospect to develop an auxiliary diagnosis system for dental periapical radiographs based on deep convolutional neural networks (CNNs), and the indications and performances should be investigated. The aim of this study is to train CNNs for lesion detections on dental periapical radiographs, to evaluate performances across disease categories, severity levels, and train strategies. Deep CNNs with region proposal techniques were constructed for disease detections on clinical dental periapical radiographs, including decay, periapical periodontitis, and periodontitis, leveled as mild, moderate, and severe. Four strategies were carried out to train corresponding networks with all disease and level categories (baseline), all disease categories (Net A), each disease category (Net B), and each level category (Net C) and validated by a fivefold cross-validation method afterward. Metrics, including intersection over union (IoU), precision, recall, and average precision (AP), were compared across diseases, severity levels, and train strategies by analysis of variance. Lesions were detected with precision and recall generally between 0.5 and 0.6 on each kind of disease. The influence of train strategy, disease category, and severity level were all statistically significant on performances (P < .001). Decay and periapical periodontitis lesions were detected with precision, recall, and AP values less than 0.25 for mild level, while 0.2-0.3 for moderate level and 0.5-0.6 for severe level. Net A performed similar to baseline (P > 0.05 for IoU, precision, and recall), while Net B and Net C performed slightly better than baseline under certain circumstances (P < 0.05), but Net C failed to predict mild decay. The deep CNNs are able to detect diseases on clinical dental periapical radiographs. This study reveals that the CNNs prefer to detect lesions with severe levels, and it is better to train the CNNs with customized strategy for each disease.

Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs.

Determining the peri-implant marginal bone level on radiographs is challenging because the boundaries of the bones around implants are often unclear or the heights of the buccal and lingual bone levels are different. Therefore, a deep convolutional neural network (CNN) was evaluated for detecting the marginal bone level, top, and apex of implants on dental periapical radiographs. An automated assistant system was proposed for calculating the bone loss percentage and classifying the bone resorption severity. A modified region-based CNN (R-CNN) was trained using transfer learning based on Microsoft Common Objects in Context dataset. Overall, 708 periapical radiographic images were divided into training (n = 508), validation (n = 100), and test (n = 100) datasets. The training dataset was randomly enriched by data augmentation. For evaluation, average precision, average recall, and mean object keypoint similarity (OKS) were calculated, and the mean OKS values of the model and a dental clinician were compared. Using detected keypoints, radiographic bone loss was measured and classified. No statistically significant difference was found between the modified R-CNN model and dental clinician for detecting landmarks around dental implants. The modified R-CNN model can be utilized to measure the radiographic peri-implant bone loss ratio to assess the severity of peri-implantitis.

Accuracy of periapical and panoramic radiography for detection of root morphologies: a comparative study.

Maxillary and mandibular incisors have increased risk for severe orthodontically induced inflammatory root resorption. A patient-related risk factor is aberrant root morphology. This study aimed to assess the frequency of detection of different root morphologies in anterior teeth using dental panoramic tomography (DPT) and long cone periapical radiographs (LCPAs). A retrospective cross-sectional design was used to assess a sample of 50 consecutive pre-treatment radiographic records of patients from a specialist orthodontic practice in Adelaide, Australia. A reference guide was developed that included three previously unreported morphologies: pipette and bent, bent and pointed, bent and blunt. Two trained and calibrated assessors examined each record against the inclusion criteria, then independently assessed each anterior tooth from DPTs and LCPAs to detect the type of root morphology present. Data were analysed using the chi-square statistical test. Radiographic records for 48 patients (48 DPTs and 161 LCPAs) were eligible, with 355 and 426 teeth on DPTs and LCPAs, respectively, included for assessment. Normal root morphology (119 teeth) was commonly observed in DPTs, while bent (154 teeth) was frequently observed using LCPAs. Mandibular incisors often had normal morphology in DPTs but bent in LCPAs. Bent was the most common morphology in maxillary lateral incisors using DPT and LCPAs, although maxillary centrals were mostly normal in DPTs but pointed in LCPAs. Differences using the two image acquisition methods were highly significant (p < 0.01). Aberrant root morphologies are more easily detected in anterior teeth using LCPAs compared to DPTs.

Satisfaction of Search in Periapical Radiograph Interpretation

IntroductionSeveral studies in radiology and medicine have evaluated the satisfaction of search (SOS) error effect in chest radiography, abdominal radiography, osteoradiology, and patients with multiple trauma. No research to date has been published evaluating the possible existence of the SOS error phenomenon made during dental periapical radiograph interpretations. The purpose of the present pilot study was to determine if an SOS error effect exists when dental clinicians interpret periapical radiographs. The null hypothesis was that the detection accuracy will be the same or will improve for the detection of native lesions in the presence of an added abnormality. The alternative hypothesis is that there will be a decrease in detection accuracy for native lesions in the presence of an added abnormality. MethodsSix images were selected to be part of the present experiment. One of the 6 images served as the positive control, and another image served as the negative control. Four images, each including a single subtle carious lesion, were selected to represent the experimental images. The single subtle carious lesion present within the 4 experimental radiographs served as the native pathology, and an abnormality such as a periapical radiolucency, resorption, inadequate nonideal root canal obturation material, or recurrent carious lesion was artificially inserted into the image as the added pathology. Thus, the second set of images consisted of the same 4 images containing the native pathology including an added pathology that was inserted into the image using Adobe Photoshop CS6 (Adobe, Inc, San Jose, CA). Purposive sampling was obtained from 16 examiners including residents from endodontics and periodontics as well as alumni and faculty from the Saint Louis University Center for Advanced Dental Education, St Louis, MO. Each observer participated as a subject during 2 time-separated sessions. Each session was separated by a minimum period of 3 months' duration in order to prevent memory bias. Before starting each interpretation session, the participants were given verbal instructions. Subjects were instructed to provide a location (by tooth number), identify, and rate the presence of all suspected pathology using a Likert scale of 1–5 (1: definitely normal, 2: probably normal, 3: possibly abnormal, 4: probably abnormal, and 5: definitely abnormal). In the second session, the radiographs that were initially presented containing only the native lesion were presented again with the added abnormality, and vice versa. The observers' reports and confidence ratings were recorded and analyzed. Ratings of 3–5 were considered as being positive for the presence of pathology. ResultsA true SOS error occurs when the presence of the native lesion is reported correctly without an added abnormality but is not reported (missed) in the presence of an added abnormality. In our study, a true SOS error occurred in 13 of the 64 interpretation sets (20.31%). There was a total of 64 expected native lesions present within the 4 native images viewed by 16 observers. In the 4 added images, there was a total of 64 expected added findings. In the images containing only native lesions, the observers reported 30 of the 64 expected native lesions. In the images containing an artificially added abnormality, the observers reported 58 of the 64 expected added abnormalities and 25 of the 64 expected native lesions. Observers reported fewer native lesions in the presence of an added abnormality. ConclusionsThe current investigation demonstrated the existence of the SOS effect during periapical radiographic interpretations. In 20.31% of interpretations, a true SOS error occurred. This study is clinically relevant because it can help clinicians in reducing false-negative errors made during radiographic interpretation, thus preventing misdiagnosis.

Automated feature detection in dental periapical radiographs by using deep learning

The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)-based computer vision techniques. Caries, alveolar bone recession, and interradicular radiolucencies were labeled on 206 digital PRs by 3 specialists (2 oral pathologists and 1 endodontist). The PRs were divided into "Training and Validation" and "Test" data sets consisting of 176 and 30 PRs, respectively. Multiple transformations of image data were used as input to deep neural networks during training. Outcomes of existing and purpose-built DL architectures were compared to identify the most suitable architecture for automated analysis. The U-Net architecture and its variant significantly outperformed Xnet and SegNet in all metrics. The overall best performing architecture on the validation data set was "U-Net+Densenet121" (mean intersection over union [mIoU]=0.501; Dice coefficient=0.569). Performance of all architectures degraded on the "Test" data set; "U-Net" delivered the best performance (mIoU=0.402; Dice coefficient=0.453). Interradicular radiolucencies were the most difficult to segment. DL has potential for automated analysis of PRs but warrants further research. Among existing off-the-shelf architectures, U-Net and its variants delivered the best performance. Further performance gains can be obtained via purpose-built architectures and a larger multicentric cohort.

Diagnostic reference levels in paediatric intra oral peri apical (IOPA) dental radiography at University Dental Hospital, Peradeniya

Dental radiography is a common diagnostic technique practiced among the paediatric patients visiting the dentist. Therefore, to abide the ALARA (as low as reasonably achievable) principal and reduce the undesired exposure especially for the pediatric patients the DRLs in paediatric intraoral periapical dental radiographs was developed using the patient entrance surface doses (ESD).

An effective teeth recognition method using label tree with cascade network structure

In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X-ray image and distinguish them from different position. An adult usually has 32 teeth, and some of them are similar while others have very different shape. So there are 32 teeth position for us to recognize, which is a challenging task. Convolutional neural network is a popular method to do multi-class detection and classification, but it needs a lot of training data to get a good result if used directly. The lack of data is a common case in medical field due to patients’ privacy. In this work, limited to the available data, we propose a new method using label tree to give each tooth several labels and decompose the task, which can deal with the lack of data. Then use cascade network structure to do automatic identification on 32 teeth position, which uses several convolutional neural network as its basic module. Meanwhile, several key strategies are utilized to improve the detection and classification performance. Our method can deal with many complex cases such as X-ray images with tooth loss, decayed tooth and filled tooth, which frequently appear on patients. The experiments on our dataset show: for small training dataset, compared to the precision and recall by training a 33-classes (32 teeth and background) state-of-the-art convolutional neural network directly, the proposed approach reaches a high precision and recall of 95.8% and 96.1% in total, which is a big improvement in such a complex task.

Assessing periapical dental radiographs as a screening parameter for early indications of osteoporosis in postmenopausal periodontal patients and root surface evaluation using spectrochemical analysis.

Objectives:To assess the usefulness of periapical dental radiograph as a screening tool aimed at early signs of osteoporosis in postmenopausal periodontal patients and root surface evaluation using spectrochemical analysis.Methods:This study was conducted at the Department of Periodontics, Riyadh Elm University, Riyadh, Saudi Arabia, for 12 months between December 2016 and November 2017. Two groups consisted healthy postmenopausal women having chronic periodontitis and postmenopausal women having chronic periodontitis with osteoporosis. Osteoporosis were evaluated for plaque index (PI); gingival index (GI); clinical attachment level (CAL); probing pocket depth (PPD), and bone mineral density (BMD). A standardized digital dental periapical radiographs were taken for every patient. The spectrochemical analysis was carried out using the self-assembled Laser-Induced Breakdown Spectroscopy (LIBS) system used for qualitative and quantitative analysis of Calcium (Ca), Potassium (K), Phosphorus (P), Fluoride (F), and Magnesium (Mg)Results:There was no statistically significant difference between both groups for GI and PI. Similarly PPD and CAL were showing the difference but statistically, significant difference was only for CAL. Value of distance starting from cement enamel junction to the alveolar crest (CEJ-AC) and BMD were having a statistically significant variance between both groups. The differences between osteoporotic and control group were statistically significant regarding Ca with the mean higher in the control group. Furthermore, the variances between the groups in both K and Mg were statistically significant with higher mean in the osteoporotic group (p<0.05).Conclusion:The clinical, radiographic, and experimental findings of this study indicated that osteoporosis has a direct effect on the progression rate of periodontal tissue destruction and dental radiographic can be suggested as a screening tool for an early sign of osteoporosis.

Visual Fixation and Scan Patterns of Dentists Viewing Dental Periapical Radiographs: An Eye Tracking Pilot Study

IntroductionThe visual search patterns of dentists and the areas that attract their attention when interpreting dental periapical radiographs are currently unknown. This research identifies areas and patterns of visual fixation when observing dental periapical radiographs. MethodsIn an observational study using eye tracking technology and a convenience sample of 44 observers, the interpretations of 4 dental periapical radiographs were recorded using Camtasia Software (TechSmith, Okemos, MI) with a gaze tracking “bubble” denoting where within the radiograph the observers' eyes gazed. The recorded observations included the scanning pattern, the area of first fixation, and revisits of areas. Also noted was whether the area of first fixation or revisit was radiopaque, radiolucent, or of normal radiodensity and whether it was a coronal or radicular area. ResultsThe first fixation is more likely to be an area of high contrast that is either radiopaque or radiolucent compared with areas that were normal or of average gray scale. Significantly more revisits occurred on areas that were radiopaque and located in the radicular area. Of the 4 categorized scanning patterns, tooth by tooth scanning predominated. ConclusionsWhen interpreting dental periapical radiographs, significantly more observers initially fixated on areas of the radiograph that were of high contrast (ie, radiopaque or radiolucent) compared with “normal areas.” A tooth by tooth scanning pattern was most commonly used.

Age estimation based on Kvaal's technique using digital panoramic radiographs.

Introduction:Age estimation is important for administrative and ethical reasons and also because of legal consequences. Dental pulp undergoes regression in size with increasing age due to secondary dentin deposition and can be used as a parameter of age estimation even beyond 25 years of age. Kvaal et al. developed a method for chronological age estimation based on the pulp size using periapical dental radiographs. There is a need for testing this method of age estimation in the Indian population using simple tools like digital imaging on living individuals not requiring extraction of teeth.Aims and Objectives:Estimation of the chronological age of subjects by Kvaal's method using digital panoramic radiographs and also testing the validity of regression equations as given by Kvaal et al.Materials and Methods:The study sample included a total of 152 subjects in the age group of 14-60 years. Measurements were performed on the standardized digital panoramic radiographs based on Kvaal's method. Different regression formulae were derived and the age was assessed. The assessed age was then correlated to the actual age of the patient using Student's t-test.Results:No significant difference between the mean of the chronological age and the estimated age was observed. However, the values of the mean age estimated by using regression equations as given previously in the study of Kvaal et al. significantly underestimated the chronological age in the present study sample.Conclusion:The results of the study give an inference for the feasibility of this technique by calculation of regression equations on digital panoramic radiographs. However, it negates the applicability of same regression equations as given by Kvaal et al. on the study population.

Detection of radiolucencies around endodontically treated teeth on routine CT scans.

This retrospective study compared computed tomography (CT) imaging to routine dental periapical radiographs in diagnosing radiolucencies around endodontically treated teeth. Of the 244 CT scans evaluated, 104 had no teeth on the scan. On the remaining 140 scans, 353 teeth fell into the following categories: 59 maxillary molars, 30 mandibular molars, 66 maxillary premolars, 56 mandibular premolars, and 141 anterior teeth. Positive and negative predictive values were calculated, as were sensitivity, specificity, and prevalence assuming the CT scan was the test standard. For the total tooth population periapical radiograph - CT slice sensitivity was 52, specificity was 90, the positive predictive value (PPV) was 97, the negative predictive value (NPV) was 25, and the prevalence 85. In the population studied, the CT scan had a greater ability to show radiolucencies that were not evident on periapical radiographs.