• Restorative Dentistry and Endodontics
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• v.44 no.3
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• pp.30.1-30.6
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• 2019

We report the surgical endodontic treatment of a maxillary first premolar with a lateral lesion that originated from an accessory canal. Although lesions originating from accessory canals frequently heal with simple conventional endodontic therapy, some lesions may need additional and different treatment. In the present case, conventional root canal retreatment led to incomplete healing with the need for further treatment (i.e., surgery). Surgical endodontic management with a fast-setting calcium silicate cement was performed on the accessory canal using a dental operating microscope. At the patient's 9-month recall visit, the lesion was resolved upon radiography.

• The Journal of the Korean dental association
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• v.57 no.7
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• pp.392-402
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• 2019

The most important part of everyday root canal treatment is diagnosis about the morphology of tooth, root and root canal. Usually this procedure is performed by visual examination and radiographic (panoramic/periapical) examination. However, 2-dimentional radiography has several limitations such as imposition of anatomic structures including buccal/lingual root canals and distortion of images. Recently, owing to the increased interest in dental implant and affordable cost of CBCT equipment, CBCT has been introduced widely in local dental clinics. CBCT is characterized by their lower radiation dose and shorter exposure time than conventional CT scan, and ability of 3-dimentional reconstruction of the dento-alveolar structure. Also in endodontic field, the data from CBCT could be very helpful in diagnosing complex root canal anatomy, apical periodontitis, cause of failure and in determining treatment plan. However, there are some limitations such as radiation dose and artifact. Therefore, clinicians should know about indication, advantages and limitations of CBCT, and properly use it for successful root canal treatment to save the natural teeth.

• Journal of Periodontal and Implant Science
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• v.36 no.1
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• pp.147-154
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• 2006

Surgical intervention in the posterior maxillary region requires detailed knowledge of maxillary sinus anatomy and the possible anatomical variations. This study evaluated the incidence, location of maxillary sinus septa by using radiographic (panoramic radiography and computed tomography) findings and comparison of panoramic radography with CT in antral anatomical variation. This study was based on data from 70 sinuses in partial dentate maxilla. The sample consisted of 61 patients(25 women and 36 men, with ages ranging between 19 and 77 years and a mean age of $49.4{\pm}11.3$ years) who were being treatment-planned to receive implant-supported restorations. First, the panoramic images were examined for the presence of antral septa by radiologist and examiner who don't know about CT findings. And incidence of antral septa was evaluated using an axial plane of CT image. The incidence of septa was compared between panoramic radiography and CT. The accuracy of the incidence was compared between radiologists and dentists. A total of 20 septa were found in 70 sinuses on CT image and the prevalence of one or more septa per sinus was found to be 28.6%. The assumed incidence of septa on panoramic radiography was $27.6%{\pm}2.2%$ in radiologist and $31.9%{\pm}5.8%$ in dentists. Erroneous diagnosis rate was 11.42% in radiologist and 15.96% in dentists. 40% of antral septa were located in the anterior(premolar) region, 30% of septa were located in the middle(first molar) and posterior(second molar) region separately. Prior to implant placement, it seems appropriate to consider panoramic radiography as a standard radiographic examination and periapical radiographs may be used to complete the findings in regions not sharply depicted in the panoramic radiograph. And cross-sectional imaging should be used in sites with severe bone loss and close proximity of the maxillary sinus.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.25 no.2
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• pp.297-308
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• 1995

This study was designed to measure the absorbed dose to organs of special interest from full mouth with intraoral film(l4 films) and to compare the five periapical techniques. Thermoluminescent crystals(TLD-100 chip) were located in brain, orbit, bone marrow of mandibular ramus, bone marrow of mandibular body, bone marrow of 4th cervical spine, parotid gland, submandibular gland and thyroid gland. X -ray machine was operated at 70kVp and round collimating film holding device(XCP) and rectangular collimating film holding device(Precision Instrument) were used. The distance from the X-ray focus to the open end of the collimator was 8 inch, 12 inch and 16 inch. The results were as follows : 1. The absorbed dose was the highest in bone marrow of mandibular body(5.656mGy) and the lowest in brain (0.050mGy). 2. Generally, the lowest absorbed dose was measured from 16 inch cylinder, rectangular collimating film holding device with paralleling technique. But, in bone marrow of mandibular body and the floor of mouth, the highest absorbed dose was measured from 12 inch cylinder, rectangular collimating film holding device with paralleling techniques. 3. Comparing of five intraoral radiographic techniques, it was appeared statistically significant reduction of the absorbed doses measured with rectangular collimating film holding device compared to XCP film holding device (P<0.05). 4. No statistically significant reduction in the absorbed dose was found as cylinder length was changed(P>0.05).

• Imaging Science in Dentistry
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• v.42 no.3
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• pp.129-137
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• 2012

Purpose: To compare different radiographic methods for assessing endodontically treated teeth. Materials and Methods: Root canal treatments were applied in 120 extracted mandibular teeth, which were divided into four groups: (1) ideal root canal treatment (60 teeth), (2) insufficient lateral condensation (20 teeth), (3) root canals filled short of the apex (20 teeth), (4) overfilled root canal treatment (20 teeth). The teeth were imaged using intraoral film, panoramic film, digital intraoral systems (CCD and PSP), CCD obtained with portable X-ray source, digital panoramic, and CBCT images obtained at 0.3 $mm^3$ and 0.2 $mm^3$ voxel size. Images were evaluated separately by three observers, twice. Kappa coefficients were calculated. The percentage of correct readings obtained from each modality was calculated and compared using a t-test (p<0.05). Results: The intra-observer kappa for each observer ranged between 0.327 and 0.849. The inter-observer kappa for each observer for both readings ranged between 0.312 and 0.749. For the ideal root canal treatment group, CBCT with 0.2 $mm^3$ voxel images revealed the best results. For insufficient lateral condensation, the best readings were found with periapical film followed by CCD and PSP. The assessment of teeth with root canals filled short of the apex showed the highest percentage of correct readings by CBCT and CCD. For the overfilled canal treatment group, PSP images and conventional periapical film radiographs had the best scores. Conclusion: CBCT was found to be successful in the assessment of teeth with ideal root canal treatment and teeth with canals filled short of the apex.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• Imaging Science in Dentistry
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• v.53 no.3
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• pp.199-207
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• 2023

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs(PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients(representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

• Journal of Periodontal and Implant Science
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• v.54 no.1
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• pp.3-12
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• 2024

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

• Journal of Dental Rehabilitation and Applied Science
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• v.32 no.1
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• pp.87-92
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• 2016

The purpose of this clinical report was to show anatomical variations in permanent maxillary second molar using computed tomography (CT). This case report describes the application of CT to detect the unusual root anatomy of maxillary second molar with 2 separate palatal roots for successful endodontic treatment procedures. The use of cone beam computed tomography (CBCT) can overcome the limitation of the periapical standard radiography caused by the overlap of buccal and secondary palatal roots.

• Imaging Science in Dentistry
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• v.35 no.3
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• pp.141-146
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• 2005

Purpose : The purpose of this study was to investigate the preliminary use of morphologic operation (MO) in analyzing trabecular pattern of alveolar bone for the predicting systemic osteoporosis. Materials and Methods : Study subjects consisted of 35 females (average age 48.5 years) and 25 males (average age 25.8 years). Bone mineral density BMD $(grams/cm^2)$ of lumbar spine and proximal femur of these subjects were measured by a dual energy X-ray absorptiometry (DEXA). Regions of interest (ROIs) were selected from the digitized periapical radiographs of subjects' posterior jaw. A custom computer program processed morphology operations of ROIs. We compared mean values of 11 MO variables according to the osteoporotic group divided by the T-scores of DEXA. We also studied correlation between radiographic density and these MO variables. Results : The mean radiographic densities insignificantly correlated with MO variables. There were statistically significant differences among the values of 9 MO variables according to the osteoporotic group. Conclusion Morphologic operation can be effective in analyzing trabecular pattern of alveolar bone for the predicting osteoporosis.