Deep learning-based detection of irreversible pulpitis in primary molars.

BACKGROUND: Changes in healthy and inflamed pulp on periapical radiographs are traditionally so subtle that they may be imperceptible to human experts, limiting its potential use as an adjunct clinical diagnostic feature. AIM: This study aimed to investigate the feasibility of an image-analysis technique based on the convolutional neural network (CNN) to detect irreversible pulpitis in primary molars on periapical radiographs (PRs). DESIGN: This retrospective study was performed in two health centres. Patients who received indirect pulp therapy at Peking University Hospital for Stomatology were retrospectively identified and randomly divided into training and validation sets (8:2). Using PRs as input to an EfficientNet CNN, the model was trained to categorise cases into either the success or failure group and externally tested on patients who presented to our affiliate institution. Model performance was evaluated using sensitivity, specificity, accuracy and F1 score. RESULTS: A total of 348 PRs with deep caries were enrolled from the two centres. The deep learning model achieved the highest accuracy of 0.90 (95% confidence interval: 0.79-0.96) in the internal validation set, with an overall accuracy of 0.85 in the external test set. The mean greyscale value was higher in the failure group than in the success group (p = .013). CONCLUSION: The deep learning-based model could detect irreversible pulpitis in primary molars with deep caries on PRs. Moreover, this study provides a convenient and complementary method for assessing pulp status.

Radiographic Diagnostic Choices of Palestinian Dentists for Implant Therapy.

Aim: To report the radiographic preferences during dental implant therapy in Palestine. Materials and Methods: Fourteen multiple-choice questions were delivered in electronic and hardcopy formats questionnaires during the Sixth International Implantology Conference (Palestine). The questions investigated the radiographic techniques that are mostly used based on various clinical scenarios and treatment phases. Results: One hundred and thirty-seven responses were captured. The majority of the participants were general dentists with implant experience (79.6%). Less than a third of the participants (27.2%) were members of the Palestinian Association of Dental Implantology. The majority (85.9%) of them have their practice in a city zone. Panoramic radiograph (PAN) combined with cone beam computed tomography (CBCT) was the most preferred radiographic technique during the planning stage. Conclusion: PAN and CBCT was the preferred choice during the planning stages. A PAN was preferred postoperatively and if no complications were associated. In the case of symptomatic patients, CBCT was the radiograph of choice.

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

OBJECTIVES: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. MATERIALS AND METHODS: Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. RESULTS: The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. CONCLUSION: The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.

Efficacy of customized crown-level position jig in measuring peri-implant crestal bone level on periapical radiographs: An in vitro study.

Background/purpose: Accuracy of using implant length on periapical radiographs as calibration reference for measurements has not been verified. This study aimed to verify the measurements of peri-implant crestal bone level (piCBL) on periapical radiographs taken by the paralleling technique and using the implant length for calibration; and to propose a customized crownlevel position (CLP) jig to improve the measurement accuracy of piCBL. Materials and methods: A typodont installed an implant and a screw-retained crown at maxillary central incisor was used. To simulate piCBL, a metal post was placed near the implant at the same height of implant platform. The CLP jig was designed and 3-dimensionally printed out to allow implant projected orthogonally on periapical film. Thirty periapical radiographs were taken using paralleling technique with and without the jig by three examiners. The implant axis and implant length on radiographs were acquired by image segmentation. The discrepancy of piCBL determination (ΔD) from these measurements were compared and further analyzed when using the implant length for calibration. Results: The piCBL measurement errors were smaller when the jig was used for all examiners (P < 0.001). The inter-rater differences were insignificant. After calibration, ΔD with and without jig were 0.09 (0.07-0.11) and 0.43 (0.38-0.49) mm, respectively. Conclusion: Conventional long-cone paralleling technique using true implant length for calibration demonstrated imprecise piCBL measurement on periapical radiographs. Transferring the implant axis to the CLP jig allowed orthogonal projection of radiography which provided reliable measurements of piCBL with an accuracy of less than 0.1 mm.

Assessment of Digital Intraoral Periapical Radiograph for the Detection of Apical Root Resorption in Inflammatory Periapical Pathologies: A Radiovisiography Study.

Introduction Resorption often takes the form of external inflammatory root resorption. Apical periodontitis or an apical cyst is the most typical cause of external inflammatory root resorption. Failure of endodontic treatment can occur if severe apical root resorption occurs.This is due to the difficulty of reaching these sites.Apical root resorption is usually discovered during routine radiographs and is usually in its later stages. If the lesion is advanced, extraction is the only viable solution. An accurate diagnosis of incipient root resorption is essential. This research is designed to analyze the effectiveness of digital intraoral periapical radiographs in assessing apical root resorption (ARR) related to periapical pathologies. Material and methods This cross-sectional radiographic observational research was conducted in a dental college and hospital in central India. Radiovisiography (RVG) images of 190 patients' teeth with inflammatory periapical pathologies were evaluated to determine the presence or absence of resorption in the apical area of the root. After the radiographic assessment of the apical root resorption, the extraction of the affected teeth was done under all aseptic conditions. The periapical tissue was sent for histological analysis and the extracted tooth sample was examined for the presence or absence of apical root resorption. Results In comparison to apical periodontitis, the proportion of severe root resorption patients was significantly higher in abscess and periapical granuloma. Using Pearson's Chi-square test, the difference in patient proportions according to the kind of resorption in the three radiological diagnosis groups was statistically significant with a p-value of 0.0058. Conclusion It was concluded that on radiographic examination, digital intraoral periapical radiographs were found to be accurate in determining periapical apical pathologies and apical root resorption.

Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs.

BACKGROUND: Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners' lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. METHODS: This study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8-1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD). RESULTS: MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05). CONCLUSIONS: The designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists' consensus.

The Performance of Paralleling Technique and Bisecting Angle Technique for Taking Periapical Radiographs: A Systematic Review.

Periapical radiography is a routine radiographic procedure performed by dentists on a daily basis. It can be taken with two techniques, the paralleling technique (P tech) and the bisecting angle technique (B tech). This systematic review aimed to identify the relevant literature, compare the use of P and B techs across various dental specialties, and determine the most appropriate technique to be used for different purposes in taking periapical radiographs. In January 2023, we searched PubMed, Web of Science, Scopus, and Google Scholar to identify the studies that compared the two radiographic techniques. The search string was: (paralleling AND ("bisecting angle" OR "bisected angle")). Manual reference tracing was also performed to identify the studies potentially missed. After screening, 26 studies were included for the qualitative review. The 26 included studies were published between 1976 and 2021. Ten of the studies were about general dentistry (dental radiology in general applications), whereas another ten studies were related to endodontics, such as working length estimation. Most studies advocated the use of the P tech for general, endodontics, implantology, and other indications. B tech was advocated for patients with a low palatal height. More future studies are needed to evaluate their performance in different scenarios with standardized equipment and radiographic positioning.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

Quantitative assessment of platelet rich fibrin for the repair of extraction socket in a rat model.

The present study evaluated the quantitative effects of platelet-rich fibrin (PRF) for the repair of extraction socket in Sprague Dawley (SD) rat model by assessing several key clinical parameters. Seventy two male SD rats were subjected to surgical extraction of the maxillary right incisor. Rats were randomly divided into four groups with eighteen rats in each group based on the treatment received: extraction socket without treatment of PRF was taken as control (group I). Extraction socket implanted with 0.1, 0.2, and 0.4 mL of PRF was taken as study groups (groups II, III, and IV). The obtained results demonstrated that, low dose of PRF efficiently enhanced the natural healing cascade. Whereas, high dose interfered with native tissue contribution and altered the natural healing process. The beneficial effects of quantity-based application of PRF may raise the possibility of a new approach as complementary therapy besides conventional treatment.

Endodontic Management of Mandibular anterior teeth and premolars with Vertucci's Type VIII canal morphology: A Rare Case.

A thorough knowledge of variable and complex tooth morphology, detailed exploration of the internal anatomy and underlying pathology, proper interpretation of radiographic images, conservative access to explore all the canals, thorough debridement and disinfection of canal system, three-dimensional seal by obturation, and good coronal seal by final restoration are essential steps in the management for a successful endodontic treatment outcome. Clinical management of rare case with extra canals in the lower anterior teeth and premolars had to undergo root canal therapy has been described. Referring to the hard-tissue repository of the human dental internal morphology, carefully interpreting multiangled radiography/cone-beam computed tomography, using tools such as magnifying loupes with illumination and ultrasonics, thermoplasticized gutta-percha system to obturate, are very helpful to the clinician can achieve this goal. This article describes and illustrates the management of a rare case with Vertucci's Type VIII canal anatomy in lower anterior teeth and premolars.

Radiografías periapicales y panorámicas como herramientas para la predicción temprana de osteoporosis / Periapical and panoramic radiographs as tools for early prediction of osteoporosis

Introducción: La osteoporosis es una enfermedad del sistema esquelético provocada por una disminución progresiva de la densidad mineral ósea y el deterioro de la microarquitectura, que aumenta el riesgo de fracturas. Por tanto, se hace necesario adoptar medidas de diagnóstico que permitan la detección temprana de alteraciones de la densidad mineral. Dado que las radiografías dentales son rutinarias y permiten examinar las estructuras óseas de los maxilares, se han propuesto como herramientas primarias de diagnóstico de osteoporosis. Objetivo: Examinar la viabilidad y el avance del uso de radiografías periapicales y panorámicas como predictoras tempranas de osteoporosis. Comentarios principales: Fue realizada una revisión bibliográfica sobre cómo las radiografías periapicales y panorámicas, junto con técnicas de aprendizaje automático e índices morfométricos, pueden ser predictores tempranos de osteoporosis. Consideraciones globales: Radiografías panorámicas y periapicales pueden ayudar en la predicción precoz de osteoporosis. Para ello el odontólogo debe contar con amplia experiencia en la interpretación de imágenes radiográficas o ser especialista en radiología oral o cirugía maxilofacial. Por otro lado, existen herramientas computacionales fundamentadas en aprendizaje automático que han mostrado resultados de identificación de osteoporosis comparables a los arrojados por radiólogos. Estas herramientas pueden servir de apoyo a profesionales menos experimentados. Los odontólogos están llamados a ser los primeros inspectores de cambios anómalos en la densidad ósea. Deben remitir oportunamente los pacientes con sospecha de osteoporosis al médico especialista(AU)

Introduction: Osteoporosis is a disease of the skeletal system caused by a gradual reduction in bone mineral density and deterioration of the microarchitecture, raising the risk of fracture. It is therefore necessary to implement diagnostic actions allowing early detection of mineral density alterations. Given the fact that dental radiographs are routine practice and make it possible to examine the bone structure of maxillae and mandibles, they have been proposed as primary tools for osteoporosis diagnosis. Objective: Examine the viability of and progress in the use of periapical and panoramic radiographs as early predictors of osteoporosis. Main remarks: A review was conducted about the combined use of panoramic and periapical radiographs. Both are machine learning techniques and morphometric indices. General considerations: Panoramic and periapical radiographs may be useful for early prediction of osteoporosis. To achieve this end, dentists should have broad experience interpreting radiographic images or be specialists in oral radiology or maxillofacial surgery. On the other hand, computer tools based on machine learning are available which have obtained results in osteoporosis identification comparable to those obtained by radiologists. Those tools may support the work of less experienced professionals. Dentists should be the first to detect anomalous bone density changes, timely referring patients suspected of osteoporosis to the corresponding specialist(AU)

Detecting Proximal Caries on Periapical Radiographs Using Convolutional Neural Networks with Different Training Strategies on Small Datasets.

The present study aimed to evaluate the performance of convolutional neural networks (CNNs) that were trained with small datasets using different strategies in the detection of proximal caries at different levels of severity on periapical radiographs. Small datasets containing 800 periapical radiographs were randomly categorized into a training and validation dataset (n = 600) and a test dataset (n = 200). A pretrained Cifar-10Net CNN was used in the present study. Different training strategies were used to train the CNN model independently; these strategies were defined as image recognition (IR), edge extraction (EE), and image segmentation (IS). Different metrics, such as sensitivity and area under the receiver operating characteristic curve (AUC), for the trained CNN and human observers were analysed to evaluate the performance in detecting proximal caries. IR, EE, and IS recognition modes and human eyes achieved AUCs of 0.805, 0.860, 0.549, and 0.767, respectively, with the EE recognition mode having the highest values (p all < 0.05). The EE recognition mode was significantly more sensitive in detecting both enamel and dentin caries than human eyes (p all < 0.05). The CNN trained with the EE strategy, the best performer in the present study, showed potential utility in detecting proximal caries on periapical radiographs when using small datasets.

Prevalence of pulp stones in IOPA radiographs.

The presence of calcifications or hard deposits within the pulp chamber of the teeth is referred to as pulp stones. There is a geographic and ethnic variation in the occurrence of these calcifications in the population. The article assessed the occurrence of pulp stones. As a secondary objective, the gender or age difference was also assessed. This study was carried out retrospectively by analyzing the archived dental radiographs. A total of 100 radiographs were taken for the analysis based on random sampling. The selected samples were scanned for the occurrence of pulp stones. From a total of 100 subjects, 15% had more than one pulp stone. This study revealed the prevalence of dental stones in the sample population was low. However, of the teeth examined, the mandibular arch had a higher incidence when compared to the maxillary arch. No significant correlation was found among gender or age. Further studies on a larger scale are required to find possible associations that would help determine clinical outcomes.

A technical report on the hamular technique for radiographing maxillary anterior teeth using a digital sensor.

OBJECTIVE: Obtaining an intraoral periapical radiograph of the maxillary anterior teeth can be quite challenging, especially in patients where a rigid digital sensor cannot adequately fit in the palatal vault. This technical report presents a periapical radiographic technique that places the sensor in the space surrounding the hamular notch to avoid the spatial restrictions of the hard palate, opening the possibility of parallel alignment of the sensor and the long axis of the tooth, perpendicular alignment of the tube-head to both, and a minimally distorted resultant radiographic image. METHOD: Four adult patients demonstrated the quantitative and qualitative features of this hamular technique (HT). A human skull demonstrated relative magnification and image quality. EXAMPLES AND RESULTS: While patient and equipment-specific, comparisons of magnification, distortion, and image quality illustrate the potential capabilities and limitations of the HT. The HT radiographs appeared to be less distorted than the variable-angle technique (VAT-a clinical approximation of the bisecting angle technique) but magnified 7.9-21.6%. A radiograph of an implant showed a 2.0% distortion over true aspect ratio. The HT radiograph from a human skull showed a slightly lighter and less sharp image than the VAT radiograph. CONCLUSION: Preliminary results showed that while the HT images were magnified, lighter, and less sharp that the VAT, they were diagnosable for caries. In all the HT images presented in this technical report, there was less distortion than comparable techniques. Notably, one HT periapical radiograph of a maxillary lateral implant showed virtually no distortion, suggesting that a nearly parallel projection was achieved using the technique. While further research is needed, this report shows that the HT has the potential to produce minimally distorted digital radiographs of anterior teeth in patients where the sensor cannot adequately fit into the palatal vault.

Analysis of mandible trabecular structure using digital periapical radiographs to assess low bone quality in postmenopausal women.

PURPOSE: To analyze the quality of mandibular trabecular structure in postmenopausal women using periapical radiographs. Postmenopausal women are subjected to low bone quality; hence, early detection methods are needed. In addition to bone mineral density (BMD), trabecular architecture must be assessed to determine bone quality. The mandible represents bone quality and allows the assessment of trabecular structure from periapical radiographs. MATERIAL AND METHODS: Lumbar (BMDL) and femoral BMD (BMDF) examinations were performed using dual-energy X-ray absorptiometry (DXA) in 31 postmenopausal women and divided into normal, osteopenia, and osteoporotic groups. Periapical radiographs were taken at both posterior sites of the mandible. The region of interest was taken 2 mm from the apical root of the first molar. Trabecular parameters consisting of trabecular thickness (Tb.Th) and bone percentage (BA/TA) were measured using BoneJ. RESULTS: Both trabecular parameters were significantly correlated with BMDF [BA/TA ​​(r = 0.3796; p < 0.05) and Tb.Th (r = 0.508; p < 0.05)]. BA/TA and Tb.Th were significantly different between the osteoporosis and normal groups (p < 0.05) contrast to osteopenia and normal groups (p > 0.05). CONCLUSION: Changes in mandibular trabeculae structure in postmenopausal women can be assessed using periapical radiographs.

Radiology in Endodontics.

Endodontics requires radiographic imaging for diagnosis, treatment planning, therapy, and follow-up. Dental radiography allows for the identification of pathologic changes in the periradicular tissues that cannot be visualized by clinical inspection. For the precise execution of endodontic therapy, regular radiographic verification of individual treatment steps is necessary. As a review for clinicians, normal and pathologic findings relevant to Endodontics are presented. Key radiographic imaging techniques, such as the paralleling and bisecting techniques, as well as horizontal and vertical eccentric radiographs, are discussed. The increasing utilization and impact of cone-beam computed tomography providing 3-dimensional volume imaging are reviewed.

Radiographic And Clinical Assessment Of Two Canals In The Maxillary Second Premolar.

BACKGROUND: The second premolar is one of the teeth which are crucial both aesthetically as well as functionally and one of the most commonly endodontically treated tooth. Aim of the study was to assess the number of canals in maxillary second premolar by clinical and radiographic evaluation in Pakistani sub population. It was a cross sectional study conducted in Endodontic Department of Rehmat Memorial Dental Teaching Hospital, Abbottabad, from January 2019 to January 2020. METHODS: One hundred and five patients were selected for the study, based on nonprobability sampling technique. All patients were examined clinically by exploration of pulp chamber followed by intra oral peri-apical radiograph to verify the clinical exploration of canals. RESULTS: One hundred and five patients (46 males (43.8%) and 59 females (56.2%)} were selected for the study. Out of total 105 patients 47 (44.8%) had one canal and 58 (55.2%) had two canals. Out of 46 males 25 (54.3%) had two canals and out of 59 females 33 (56.9%) had two canals. Statistical analysis showed no significant difference (p=0.1871) of canals arrangements between genders. CONCLUSION: Clinicians should be careful whenever doing root canal treatment of maxillary second premolars because of the extreme variability of the anatomy of those teeth, there is always risk of missing the second canal. Frequency of two canals was high, which is not age or gender dependant.

Diagnostic accuracy of periapical radiograph, cone beam computed tomography, and intrasurgical linear measurement techniques for assessing furcation defects: a longitudinal randomised controlled trial.

OBJECTIVES: The aim of this study was to evaluate the accuracy of cone beam computed tomography (CBCT), periapical radiograph, and intrasurgical linear measurements in the assessment of molars with furcation defects. MATERIALS AND METHODS: This parallel, single-blinded, randomised controlled trial (RCT) consisted of 22 periodontitis patients who had molar with advanced furcation involvement (FI). All patients followed the same inclusion criteria and were treated following the same protocol, except for radiographic evaluation (CBCT vs. periapical). This study proposed and evaluated five parameters that represent the extent and severity of furcation defects in molars teeth, including CEJ-BD (clinical attachment loss), BL-H (depth), BL-V (height), RT (root trunk), and FW (width). RESULTS: There were no statistically significant differences between CBCT and intrasurgical linear measurements for any clinical parameter (p > 0.05). However, there were statistically significant differences in BL-V measurements (p < 0.05) between periapical and intrasurgical measurements in maxillary molars. Meanwhile, the sensitivity were 62.8% and 56.9% for CBCT and periapical, respectively. CONCLUSIONS: Overall, when compared to the intrasurgical measurements, CBCT provided better diagnostic, sensitivity, and quantitative information on CAL, height, depth, and width of the furcation defects than periapical radiograph. CLINICAL RELEVANCE: An accurate presurgical furcation diagnostic can guide the clinicians from the stage of diagnosis to definitive management so that unnecessary periodontal surgical interventions can be prevented.