Digital Dental Imaging Research Articles

Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.

Knowledge, Attitude, and Practice of Teledentistry in Periodontal Diagnosis Among Dental Interns at a College in Sebha, Libya: A Cross-Sectional Questionnaire Study.

Background Teledentistry, a subspecialty of telemedicine dedicated to dentistry, has shown promise in improving access to dental care, particularly in rural and isolated areas.It integrates digital and telecommunication technology with dentistry, allowing for the remote distance exchange of relevant clinical information and digital dental imaging for dental consultation and treatment planning. Periodontal disease diagnosis is crucial for effective treatment and prevention of irreversible loss of periodontal structures. Early identification of periodontal disease can be pivotal in preventing periodontal tissue destruction and tooth loss and improving the overall quality of patients' lives. Sebha is a city located in the Fezzan region of southwestern Libya. It is the capital of the Sabha District and the Sabha Governorate. The city is situated in the Libyan part of the Sahara desert and is known for its strategic location as a gateway to the Sahara desert. However, there is a lack of information on the use of teledentistry in Libya in general and the use of teleperiodontics, especially in periodontal diagnosis. Hence, the aim of this questionnaire study was to evaluate knowledge, attitudes, and practice of teledentistry among dental interns at Sebha, Libya. Materials and methods A paper-based questionnaire consisting of 28 close‑ended Likert scale questions, including sections assessing the knowledge, attitude, and practice of teledentistry and teleperiodontics, was administered to dental interns at the Faculty of Dentistry, Sebha University, Sebha, Libya. Results The study surveyed 42 dental interns of the Faculty of Sebha, Libya, in total, with an 82.35% response rate among them. The majority of participants (59.5%) felt that teledentistry is reliable in arriving at periodontal diagnosis. The majority of participants (64.3%%) also had acceptable levels of trust in teledentistry equipment. However, over 45% percent of dental practitioners voiced their worries about patient privacy. Most of the participants suggested using teledentistry in some form in their future practice. Conclusion Teledentistry and its branch teleperiodontics are recent developments and its penetration among dental healthcare workers, and their knowledge, attitude, and practice remain to be thoroughly understood. The changing trends in attitudes and practice as a consequence of changes in Internet and technological awareness and the effects of the pandemic warrant closer observation and study.

Reliability of Artificial Intelligence-based Cone Beam Computed Tomography Integration with Digital Dental Images.

This study aimed to introduce cone-beam computed tomography (CBCT) digitization and integration of digital dental images (DDI) based on artificial intelligence (AI)-based registration (ABR) and to evaluate the reliability and reproducibility using this method compared with those of surface-based registration (SBR). This retrospective study consisted of CBCT images and DDI of 17 patients who had undergone computer-aided bimaxillary orthognathic surgery. The digitization of CBCT images and their integration with DDI were repeated using an AI-based program. CBCT images and DDI were integrated using a point-to-point registration. In contrast, with the SBR method, the three landmarks were identified manually on the CBCT and DDI, which were integrated with the iterative closest points method. After two repeated integrations of each method, the three-dimensional coordinate values of the first maxillary molars and central incisors and their differences were obtained. Intraclass coefficient (ICC) testing was performed to evaluate intra-observer reliability with each method's coordinates and compare their reliability between the ABR and SBR. The intra-observer reliability showed significant and almost perfect ICC in each method. There was no significance in the mean difference between the first and second registrations in each ABR and SBR and between both methods; however, their ranges were narrower with ABR than with the SBR method. This study shows that AI-based digitization and integration are reliable and reproducible.

A simplified low-cost phantom for image quality assessment of dental cone beam computed tomography unit.

A standardised testing protocol for evaluation of a wide range of dental cone beam computed tomography (CBCT) performance and image quality (IQ) parameters is still limited and commercially available testing tool is unaffordable by some centres. This study aims to assess the performance of a low-cost fabricated phantom for image quality assessment (IQA) of digital CBCT unit. A customised polymethyl methacrylate (PMMA) cylindrical phantom was developed for performance evaluation of Planmeca ProMax 3D Mid digital dental CBCT unit. The fabricated phantom consists of four different layers for testing specific IQ parameters such as CT number accuracy and uniformity, noise and CT number linearity. The phantom was scanned using common scanning protocols in clinical routine (90.0 kV, 8.0 mA and 13.6 s). In region-of-interest (ROI) analysis, the mean CT numbers (in Hounsfield unit, HU) and noise for water and air were determined and compared with the reference values (0 HU for water and -1000 HU for air). For linearity test, the correlation between the measured HU of different inserts with their density was studied. The average CT number were -994.1 HU and -2.4 HU, for air and water, respectively and the differences were within the recommended acceptable limit. The linearity test showed a strong positive correlation (R2 = 0.9693) between the measured HU and their densities. The fabricated IQ phantom serves as a simple and affordable testing tool for digital dental CBCT imaging.

Computer-guided miniscrew insertion in the paramedian and parapalatal area of the palatal vault: low failure rate and no learning curve required to obtain predictable results?

To evaluate the failure rate of palatal computer-guided miniscrews, placed in paramedian and parapalatal regions for orthodontic purposes. In addition, to investigate the presence of a learning curve using computer guided miniscrew insertion, and to evaluate the peri-implant soft tissues response at 2-, 6- and 12- month follow-ups. 202 palatal computer-guided miniscrews were inserted in 78 subjects for orthodontic purposes. A surgical guide was designed after planning the appropriate insertion sites on three-dimensional images created by the fusion of cone-beam computed tomography (CBCT) and digital dental model images. The devices were disassembled monthly to perform the percussion test and to evaluate the mobility of each miniscrew. To determine the presence of a learning curve, the time of miniscrew failures and the number of surgeries were evaluated. Bleeding on probing (BOP) and probing pocket depth (PPD) were recorded for each miniscrew, at 2- (T0), 6- (T1), and 12-month follow ups (T2). An immediate failure rate of 4.95%, due to lack of primary stability immediately following miniscrew insertion, was recorded, with statistically significant higher failure rate of parapalatal miniscrews (P= 0.00). Miniscrew failure occurred at random time, with an absence of a learning curve. The BOP (mean: 3.13%) and PPD (mean: 1.68mm) measurements remained stable over time. Computer-guided miniscrew insertion in the palatal vault showed a low failure rate without a determined learning curve to obtain predictable results and with long-term stability of peri-implant soft tissues indexes.

Awareness and practice pertaining to the use of digital imaging for orthodontic purposes among undergraduate dental students.

The current study aimed at evaluation of the awareness of undergraduate dental students regarding the use of digital imaging that are used for orthodontic diagnosis and treatment. An online survey using google forms was conducted among the undergraduate dental students as a part of this study setting. The sample size of participants was 109. The survey was composed of a set of 14 questions including demographics. Ethical clearance was obtained from the Institutionalized ethical committee. SPSS software was used for data analysis and descriptive statistics. Among the entire population, 46.2% participants said that they would be using digital dental imaging during their practices and in their career and the remaining 53.8% participants said that they would not be using digital imaging due to various reasons. This study found out that undergraduate dental students have an average level of awareness regarding the use of digital dental imaging that are used for orthodontic diagnosis and treatment.

Recognition of Digital Dental X-ray Images Using a Convolutional Neural Network.

Digital dental X-ray images are an important basis for diagnosing dental diseases, especially endodontic and periodontal diseases. Conventional diagnostic methods depend on the experience of doctors, so they are highly subjective and consume more energy than other approaches. The current computer-aided interpretation technology has low accuracy and poor lesion classification. This study proposes an efficient and accurate method for identifying common lesions in digital dental X-ray images by a convolutional neural network (CNN). In total, 188 digital dental X-ray images that were previously diagnosed as periapical periodontitis, dental caries, periapical cysts, and other common dental diseases by dentists in Qilu Hospital of Shandong Universitywere collected and augmented. The images and labels were inputted into four CNN models for training, including visual geometry group (VGG)-16, InceptionV3, residual network (ResNet)-50, and densely connected convolutional networks (DenseNet)-121. The average classification accuracy of the four trained network models on the test set was 95.9%, while the classification accuracy of the trained DenseNet-121 network model reached 99.5%. It is demonstrated that the use of CNNs to interpret digital dental X-ray images is an efficient and accurate way to conduct auxiliary diagnoses of dental diseases.

Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging

BackgroundThis study aims to propose the combinations of image processing and machine learning model to segment the maturity development of the mandibular premolars using a Keras-based deep learning convolutional neural networks (DCNN) model.MethodsA dataset consisting of 240 images (20 images per stage per sex) of retrospect digital dental panoramic imaging of patients between 5 and 14 years of age was retrieved. In image preprocessing, abounding box with a dimension of 250 × 250 pixels was assigned to the left mandibular first (P1) and second (P2) permanent premolars. The implementation of dynamic programming of active contour (DP-AC) and convolutions neural network on images that require the procedure of image filtration using Python TensorFlow and Keras libraries were performed in image segmentation and classification, respectively.ResultsImage segmentation using the DP-AC algorithm enhanced the visibility of the image features in the region of interest while suppressing the image's background noise. The proposed model has an accuracy of 97.74%, 96.63% and 78.13% on the training, validation, and testing set, respectively. In addition, moderate agreement (Kappa value = 0.58) between human observer and computer were identified. Nonetheless, a robust DCNN model was achieved as there is no sign of the model's over-or under-fitting upon the learning process.ConclusionsThe application of digital imaging and deep learning techniques used by the DP-AC and convolutions neural network algorithms to segment and identify premolars provides promising results for semi-automated forensic dental staging in the future.

Gender classification on digital dental x-ray images using deep convolutional neural network

Human teeth are more durable in nature and tend to survive even after the death of a person. For this reason, teeth play a momentous role in the forensic department to identify a missing or dead person. Gender discrimination is also a key process in forensic analysis. However, designing an automated dental gender estimation based on deep learning techniques still needs more research efforts. This paper proposed an algorithm to estimate the gender of a human from dental x-ray image (DXI). It consists of three steps namely pre-processing, segmentation, and gender classification. In the initial step, the DXI is denoised to remove the unwanted noises such as Gaussian, speckle, and impulse using a prime magic square filter. In the second step, the gradient-based recursive thresholding segmentation is used to segment the denoised DXI. At the final stage, the image is classified using the Resnet50 deep convolutional neural network classification algorithm. The database of 1000 dental images has taken and divided into 600 training samples and 400 testing samples during experimental evaluation. Various performance measures of the proposed segmentation and classification method are evaluated. The proposed system has achieved 98.27% of classification accuracy. Moreover, the performance of the proposed work is compared with similar existing methods. It shows that the proposed system produced better results. Therefore, the proposed gender classification system supports the dentist in forensic applications to record the dental post mortem details in case of a big disaster.

Spatial frequency domain analysis of a commercially available digital dental detector

X-ray detectors are used in medical imaging for the representation of diagnostic information. Digital detector performance is evaluated through appropriate parameters in the spatial domain (i.e. contrast, noise or resolution) and in the frequency domain. Dental radiography is a domain of medical imaging. The purpose of this work is the examination of a commercially available digital dental imaging detector through spatial frequency domain parameters. The available detector was a commercially available image receptor SCHICK CDR, working in indirect mode that is a scintillator coupled to a Complementary Metal Oxide Semiconductor (CMOS) photoreceptor. The detector was irradiated at an X-ray system with 60 kVp and 70 kVp tube voltages utilized in intraoral radiography. The detector linearity, Modulation Transfer Function (MTF), Normalized Noise Power Spectrum (NNPS) and Detective Quantum Efficiency (DQE) were measured according to literature. The Entrance Surface Air Kerma (ESAK) was measured with an RTI PIRANHA X-ray multimeter. The images were evaluated as presented to the dentist by the detector software, in 12bit format. The resolution of the detector was found better than 100 μm. The DQE curves suggested optimum exposure conditions below 133 μGy.

Effects of number of metal restorations and mandibular position during computed tomography imaging on accuracy of maxillofacial models

Computed tomography (CT) imaging for three-dimensional (3D) printed models may improve the quality of surgical preoperative plans. Although metal objects can cause artifacts in CT images, integration of CT and digital dental arch imaging may solve this problem. The present study aimed to evaluate effects of the number of metal restorations and mandibular position during CT imaging on accuracy of reproduced models. Stereolithography datasets from three sets of dental models having different numbers of metal restorations were obtained using a laboratory digitizing device (control) and CT equipment (nonintegrated data). CT scanning was performed under two conditions: intercuspal position (closed) and separated using paraffin wax (open). Nonintegrated data after metal artifact removal were separated into maxillary and mandibular dentition groups. The occlusal part of the control dentition and nonintegrated data were superimposed and integrated (integrated data). The root mean square (RMS) between the control and stereolithography data was calculated and analyzed with three-way analysis of variance and t-test with Bonferroni correction. Increasing numbers of metal restorations resulted in increase in metal artifacts and RMS values. Moreover, the RMS of the closed dataset was significantly greater than that of the open dataset because of the creation of artificial occlusal surfaces. The RMSs of the integrated datasets were significantly smaller than those of the nonintegrated datasets, except for the open model without metal restorations. Accuracies of reproduced maxillary and mandibular models decreased with increasing numbers of metal restorations and in the closed mandibular position during CT scanning.

Osteoporosis identification based on the validated trabecular area on digital dental radiographic images

Research for identifying osteoporosis using dental radiographic images is increasing rapidly. Subjects data from various regions and countries have been used by many researchers. This indicates that osteoporosis has become a widespread disease that should be studied more deeply. A method for osteoporosis identification based on the validated trabecular area present on digital dental radiographic images is proposed in this paper. Digital dental radiographic images of subjects were first prepared. This study performs a sequence of morphological operations to obtain the region of interest (RoI) from the validated trabecular area on the images. The validated area is then evaluated using dice similarity method. Bone mineral density is measured using dual X-ray absorptiometry at two sites to assess the presence of osteoporosis. We propose four statistical features, namely deviation, entropy, homogeneity, and correlation, which are extracted from the RoIs. These four features are obtained through feature extraction followed by feature selection using the C4.5 feature selection method. Thereafter, multilayer perceptron is used to predict the presence of osteoporosis by statistical texture analysis. The average dice similarity coefficient for all of RoIs achieves an index of 0.8924. Multilayer perceptron classifier is an appropriate method in our proposed work, which achieves an accuracy of 87.87%. This research shows that the proposed method using the sequence of morphological operations achieves high similarity for forming validated trabecular area and the four statistical features achieves a good performance for osteoporosis identification.

Palatal orthodontic miniscrew insertion using a CAD-CAM surgical guide: description of a technique

The aim of this report was to describe a new computer-guided technique for a controlled site preparation and palatal orthodontic miniscrew insertion using a dedicated software. A surgical guide was designed after planning the appropriate insertion sites on three-dimensional images created by the fusion of cone-beam computed tomography (CBCT) and digital dental model images. Pre- and postoperative CBCT images were compared and the angular, coronal, and apical deviations between the planned and the placed miniscrews were calculated. The mean coronal and apical deviations were 1.38mm (range: 3.48–0.15mm; standard deviation (SD): 0.65) and 1.73mm (range: 5.41–0.10mm; SD: 1.03), respectively, while the mean angular deviation was 4.60° (range: 15.23–0.54°; SD: 2.54). The present surgical guide allows a controlled and accurate palatal miniscrew placement in three dimensions.

Comparison of digital dental images yielded by digital dental casts, cone-beam computed tomography, and multislice computed tomography for measurement of dental area

Objectives We investigated and compared the errors generated by multislice computed tomography (MSCT), cone-beam computed tomography (CBCT), and digital dental casts when used to provide digital data about dental structures.

Effects of radiation doses from different dental imaging modalities on cell-implant interaction: A comparison with cell culture study

Objectives: The effect of radiation after exposing with dental imaging devices on cell-implant material interaction has not been adequately studied. The aim of this study is to determine the effects of radiation by measuring proliferation and differentiation of the human fetal osteoblast (hFOB) cells using dental imaging techniques and evaluate the result in vitro conditions at cellular stage. Materials and Methods: hFOB were irradiated 1 day after planting on commercially pure titanium discs. Radiation was single dose in one exposure using five different dental imaging techniques as conventional dental and digital dental X-ray tubes, conventional panoramic, digital panoramic and cone beam computerized tomography (CBCT). In the 8 th day of experiment, exposured cells and nonexposured control group cells which planted on discs were compared and examined regarding their proliferations and alkaline phosphatase (ALP) activities. Results were evaluated using Student's Independent t-test in SPSS 15.0 (SPSS Inc., Chicago, IL, USA). Results: In exposure with digital dental imaging device, ALP activities of cells are increased according to the control group and statistically, with 0, 2 s of exposure, a significant increment was found (P < 0.05). The exposures with CBCT and conventional dental imaging devices decreased cell proliferation compared with the control groups, but it was found statistically insignificant. In exposure with the other dental imaging devices, cell proliferation increased insignificantly. Conclusions: Our in vitro study suggests that the ionizing radiation produced by digital dental imaging devices may result to certain increment of the cell number and activities. More controlled study should be made with cell cultures in order to verify the exact activity of digital dental imaging.

Evaluation of the Course of the Inferior Dental Canal Using Digital Dental Panoramic Imaging

Background The position and appearance of the inferior dental canal (IDC)areimportant consideration during a surgical treatment of the mandible. Objective To study the appearance of the IDC radiographically with the aid of histogram equalization of digital imaging to improve the vision of the IDC outline on the digital dental panoramic images in the posterior area of the lower jaw. Method 150 digital panoramic images ofthe right side of the mandible were evaluated according to a particular inclusive criteria.The percentage of the IDCwas calculated to assist the histogram equalization usefulness in the vision of the canal. Result The results showed that there was a difference in the appearance of IDC on the panoramic images of the mandible. The evaluation of the histogram equalization of the digital imageshowed that the canal visualization improved in twenty-seven images,representing 52%,and twelve images,representing 23%, did not show any improvement. The researcher was no sure of thirteen images, representing 25%. Conclusion Since the radiographic appearance of IDC is not universal, and the evaluation of the histogram equalization of the digital image found to be useful in some cases, but not in all; therefore, there is a need for further investigation using advanced modalities such as cone beam CT.

Intraoral Radiology in General Dental Practices – A Comparison of Digital and Film-Based X-Ray Systems with Regard to Radiation Protection and Dose Reduction

The purpose of this study was to gain insight into the distribution and application of digital intraoral radiographic techniques within general dental practices and to compare these with film-based systems in terms of patient dose reduction. 1100 questionnaires were handed out to general dental practitioners. Data was analyzed with respect to the type of system by using descriptive statistics and nonparametric tests, i.e. Kruskal-Wallis, Mann-Whitney and chi-square test (SPSS 20). 64% of the questioned dentists still use film-based radiology, 23% utilize storage phosphor plate (SPP) systems and 13% use a charge-coupled device (CCD). A strong correlation between the number of dentists working in a practice and the use of digital dental imaging was observed. Almost 3/4 of the film users work with E- or F-speed film. 45% of them refuse to change to a digital system. The use of lead aprons was popular, while only a minority preferred thyroid shields and rectangular collimators. A fourfold reduction of exposure time from D-speed film to CCD systems was observed. Due to detector size and positioning errors, users of CCD systems take significantly more single-tooth radiographs in total. Considering the number of radiographs per patient, there is only a slight tendency towards more X-rays with CCD systems. Up to image generation, digital systems seem to be as or even more difficult to handle than film-based systems, while their handling was favored after radiographic exposure. Despite a slight increase of radiographs taken with CCD systems, there is a significant dosage reduction. Corresponding to the decrease in exposure time, the patient dose for SPP systems is reduced to one half compared to film. The main issues in CCD technology are positioning errors and the size of the X-ray detectors which are difficult to eliminate. The usage of radiation protection measures still needs to be improved. ► Responsible use of digital intraoral radiology results in a significant dose reduction in everyday practice. ► The ALARA principle is only achieved by strict implementation of dose-reducing methods. ► The efforts to use dose-reducing devices must be increased.

Quality assurance in digital dental imaging: a systematic review

Objectives. Doses induced by individual dental examinations are low. However, dental radiography accounts for nearly one third of the total number of radiological examinations in the European Union. Therefore, special attention is needed with regard to radiation protection. In order to lower patient doses, the staff performing dental examinations must have competence in imaging as well as in radiation protection issues. This paper presents a systematic review about the core competencies needed by the healthcare staff in performing digital dental radiological imaging quality assurance. Materials and methods. The following databases were searched: Pubmed, Cinahl, Pro Quest and IEEXplore digital library. Also volumes of some dental imaging journals and doctoral theses of the Finnish universities educating dentists were searched. The search was performed using both MeSH terms and keywords using the option ‘search all text'. The original keywords were: dental imaging, digital, x-ray, panoramic, quality, assurance, competence, competency, skills, knowledge, radiographer, radiologist technician, dentist, oral hygienist, radiation protection and their Finnish synonyms. Results. Core competencies needed by the healthcare staff performing digital dental radiological imaging quality assurance described in the selected studies were: management of dental imaging equipment, competence in image quality and factors associated with it, dose optimization and quality assurance. Conclusions. In the future there will be higher doses in dental imaging due to increasing use of CBCT and digital imaging. The staff performing dental imaging must have competence in dental imaging quality assurance issues found in this review. They also have to practice ethical radiation safety culture in clinical practice.

DICOM Awareness of Oral and Maxillofacial Radiologists in India

DICOM Awareness of Oral and Maxillofacial Radiologists in India

Quality assurance phantom for digital dental imaging

The purpose of this research was to develop a simple quality assurance phantom that could be used for the initial calibration and follow-up testing of commercially available intraoral digital imaging systems. A radiographic phantom was constructed that contains a calibrated step wedge for measuring dose response, an etched pattern of slits in a metallic background for measuring the spatial resolution in line pairs per millimeter, and 2 rows of wells of varying diameter and depth in an acrylic background for contrast-detail analysis. Quality assurance protocols were developed and validated. The quality assurance phantom provides a method of assessing a digital intraoral imaging system by measuring the sensitivity and dynamic range, the contrast/detail detectability and the spatial resolution. This quality assurance phantom can serve as an effective means to calibrate and monitor the performance characteristics of a digital dental intraoral imaging system.