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Effect of Field Of View on Detection of External Root Resorption in Cone-Beam Computed Tomography.

ABSTRACT: Conventional methods for diagnosis of external root resorption (ERR) are based on clinical findings and x-ray observations which are not appropriate for early diagnosis. The present study assessed the effect of different sizes and field of views (FOVs) in the diagnosis of simulated external root resorption by cone-beam computed tomography (CBCT).In this diagnostic in vitro trial, 100 human extracted mandibular central incisors were collected and marked in 3 apical, middle and coronal areas. Cavities with different sizes were created in buccal and lingual surfaces of each area. Following this procedure, CBCT images were taken in 2 different 6 × 6 cm and 12 × 8 cm FOVs with the same voxel size of 0.2 mm. Absence or presence of cavities in CBCT images were assigned by 3 radiologists and compared with gold standard results which were obtained by measurement of the size of cavities using a digital caliper. Sensitivity and specificity values, positive predictive value (PPV) and negative predictive value (NPV), AZ value and Kappa values were calculated and reported.Amounts of sensitivity in 6 × 6 cm FOV with voxel size of 0.2 mm for small, medium and large cavities were 95.93%, 96.03% and 97.1%, respectively. Amounts of sensitivity in 12 × 8cm FOV with the same voxel size for small, medium and large cavities were noted as 94.4%, 96.03% and 98.5%, respectively. However, specificity in FOV of 6 × 6 cm and FOV of 12 × 8 cm was calculated as 93.03% and 90.83%, respectively.Both used FOVs show nearly same performances in the case of detection of ERR; therefore, smaller FOV should be preferably used for detection of ERR in order to decrease the amount of imposed radiation dose given to patients.

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PROVIDER: S-EPMC5431714 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:ObjectivesCanine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectively. The aim of this study was to use deep learning to automatically evaluate the diagnosis of CIRR in maxillary incisors using CBCT images.MethodsA total of 50 cone beam computed tomography (CBCT) images and 176 incisors were selected for the present study. The maxillary incisors were manually segmented and labeled from the CBCT images by two independent radiologists as either healthy or affected by root resorption induced by the impacted canines. We used five different strategies for training the model: (A) classification using 3D ResNet50 (Baseline), (B) classification of the segmented masks using the outcome of a 3D U-Net pretrained on the 3D MNIST, (C) training a 3D U-Net for the segmentation task and use its outputs for classification, (D) pretraining a 3D U-Net for the segmentation and transfer of the model, and (E) pretraining a 3D U-Net for the segmentation and fine-tuning the model with only the model encoder. The segmentation models were evaluated using the mean intersection over union (mIoU) and Dice coefficient (DSC). The classification models were evaluated in terms of classification accuracy, precision, recall, and F1 score.ResultsThe segmentation model achieved a mean intersection over union (mIoU) of 0.641 and a DSC of 0.901, indicating good performance in segmenting the tooth structures from the CBCT images. For the main classification task of detecting CIRR, Model C (classification of the segmented masks using 3D ResNet) and Model E (pretraining on segmentation followed by fine-tuning for classification) performed the best, both achieving 82% classification accuracy and 0.62 F1-scores on the test set. These results demonstrate the effectiveness of the proposed hierarchical, data-efficient deep learning approaches in improving the accuracy of automated CIRR diagnosis from limited CBCT data compared to the 3D ResNet baseline model.ConclusionThe proposed approaches are effective at improving the accuracy of classification tasks and are helpful when the diagnosis is based on the volume and boundaries of an object. While the study demonstrated promising results, future studies with larger sample size are required to validate the effectiveness of the proposed method in enhancing the medical image classification tasks.

Project description:PurposeAnastomosis between posterior superior alveolar artery and infraorbital artery can go through bony canal in the lateral wall of the maxilla. This artery is called alveolar antral artery. It can complicate lateral sinus lift procedure by bleeding and hemosinus formation or bone graft wash out. The artery can also go in soft tissues where is not visible on cone beam computed tomography. In previous studies, the relation of this artery to sinus floor or alveolar process was measured. These structures are highly unstable during lifetime and after tooth loss. The aim of this study is to study presence and relations of bony canal in the lateral maxillary wall, to characterize the group of patients which is more likely to have bone canal in the lateral maxilla. The aim and the novelty of this study is the describing of the relationship of the bony canal to the more stable structure of hard palate and describing the relation of presence of bony canal on width of maxillary sinus, and to facilitate the prediction of presence of the alveolar antral artery.MaterialsThe cone beam computed tomography scans of the patients (251 in number) of the university hospital were examined for presence of alveolar antral artery (148 was fulfilled inclusion criteria), patient were characterized by gender, age, and sinus type (wide, average, narrow). The diameter of the bony canal and its relation to the level of sinus floor and hard palate were measured.ResultsThe cone beam computed tomography scans of 148 patients, out of it 55 man (37,2%) and 93 women (62,8%). Bony canal containing alveolar anastomosis was found in 69 cases (57,0%). Presence of the bony canal in the lateral wall of maxillae showed statistical probability depending on age with p = 0, 064 according to Mann-Whitney test. The older patients have more likely the bony canal. The presence of the alveolar antral artery was found more likely in the wide sinuses. The hard palate level can serve as a prediction point of alveolar antral artery only in first molar and second premolar region. In accordance with previous studies the width of bony canal is significantly higher in group of man (p = 0, 015). There was found a correlation between smaller distance of bony canal from sinus floor in the presence of teeth (p = 0, 067). After tooth loss the distance between sinus floor and bony canal increases, but the distance of bony canal to hard palate level stays constant. This can be explained hypothetically so that periodontal ligaments and root surface acts as a barrier for sinus pneumatization.ConclusionLateral sinus lifting in some cases can be unenviable, the knowledge about alveolar antral artery anatomy can reduce the risk of arterial bleeding. The cone-beam computed tomography is a routine examination prior to augmentation surgery and therefore the data obtained from it has an impact on clinical practice.

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Effect of Field Of View on Detection of External Root Resorption in Cone-Beam Computed Tomography.

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