The objective of this study was to evaluate whether linear accuracy and metallic scatter artifact generated between 360 degree and 180 degree Cone Beam Computed Tomography (CBCT) acquisition protocols were significantly different during evaluation of peri-implant bone levels. On ten dentate dry human skulls, dental implants were placed at two posterior mandibular implant sites in each skull, one on left and one on right, totaling 20 sites for each protocol. CBCT scans using both acquisition protocols were made on each site. The conventional 360 degree protocol contained: (Parameters: (90 kV -10 mA), 120mm x 100mm focused field of view (FOV) and 17.5 (s) exposure). The low radiation dose 180 degree protocol contained: (parameters: (80kV -2mA), 120x 100mm FOV and 9.0 (s) exposure). There was a significant difference between metallic scatter acquired by pixel intensity values during bone density measurements between 360 degree acquisition and the 180 degree acquisition (Mesial Left p=0.00; Mesial Right =0.00; Distal Left 0.51; Distal Right p=0.02; Buccal Left p=0.00; Buccal Right p=0.43; Lingual Left p=0.00, Lingual Right p=0.03). There was no significant difference in linear accuracy of measurements (Mesiodistal Left p=0.36; Mesiodistal Right p=0.13; Buccolingual Left p=0.70; Buccolingual Right p=0.92). Sensitivity and specificity of both acquisition protocols were comparable. These results dictate that low dose acquisition protocol has comparable linear measurement accuracy to conventional acquisition protocol. Additionally, there is a significant decrease in the metallic scatter, thus making the low dose 180 degree protocol significantly better for evaluating peri-implant bone levels following dental implant placement.
Permanent canines are the second most commonly impacted teeth after third molars with females being affected twice as much as males. Impacted canines can be located buccal, palatal or mid-alveolar and further be placed mesially, distally, horizontal, or inverted. Traditionally, permanent canines are radiographically localized using Clark’s method where a straight periapical radiograph of the area of interest/canine is taken, then the tube is shifted either mesial or distal to take a second radiograph. Another approach to localize an impacted canine could use a panoramic radiograph. Both of these 2D methods do not adequately depict the location of the tooth. To be able to localize the canine correctly is important for surgical exposure for further orthodontic treatment. More adequate imaging is 3D imaging in which a 360 degree Cone Beam CT (CBCT) is generally used, however, a different protocol using a 180 degree technique can reduce the radiation dose by 40%. This is important as it would limit the exposure of radiologically sensitive organs in the head and neck region.
The conventional approach for diagnosing dental caries is clinical examination and supplemented by radiographs. However, studies based on the clinical and radiographic examination methods often show low sensitivity and high specificity. Machine learning and deep learning techniques can be used to enhance optical coherence tomography (OCT) to more accurately identify diseased and damaged tissue. In this paper, we present a novel approach combining OCT imaging modality and deep convolutional neural network (CNN) for the detection of occlusal carious lesions. A total of 51 extracted human permanent teeth were collected and categorized into three groups: Non-carious teeth, caries extending into enamel, and caries extending into dentin. In data acquisition and ex-vivo OCT imaging, the samples were imaged using spectral-domain OCT system operating at 1300nm center wavelength with a scan rate of 5.5-76kHz, and axial resolution of 5.5μm in air. To acquire images with minimum inhomogeneity, imaging was performed multiple times at different points. For deep learning, OCT images of extracted human carious and non-carious teeth were input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN model employs two convolutional and pooling layers to extract features and then classify each patch based on the probabilities from the SoftMax classification layer. The sensitivity and specificity of distinguishing between carious and non-carious lesions were found to be 98% and 100%, respectively. This proposed deep learning-based OCT method can reliably classify the oral tissues with various densities, and could be extremely valuable in early dental caries detection.
Dental caries is a microbial disease that results in localized dissolution of the mineral content of dental tissue. Despite
considerable decline in the incidence of dental caries, it remains a major health problem in many societies. Early
detection of incipient lesions at initial stages of demineralization can result in the implementation of non-surgical
preventive approaches to reverse the demineralization process. In this paper, we present a novel approach combining
deep convolutional neural networks (CNN) and optical coherence tomography (OCT) imaging modality for
classification of human oral tissues to detect early dental caries. OCT images of oral tissues with various densities were
input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN
automatically learns a hierarchy of increasingly complex features and a related classifier directly from training data sets.
The initial CNN layer parameters were randomly selected. The training set is split into minibatches, with 10 OCT
images per batch. Given a batch of training patches, the CNN employs two convolutional and pooling layers to extract
features and then classify each patch based on the probabilities from the SoftMax classification layer (output-layer).
Afterward, the CNN calculates the error between the classification result and the reference label, and then utilizes the
backpropagation process to fine-tune all the layer parameters to minimize this error using batch gradient descent
algorithm. We validated our proposed technique on ex-vivo OCT images of human oral tissues (enamel, cortical-bone,
trabecular-bone, muscular-tissue, and fatty-tissue), which attested to effectiveness of our proposed method.
In this paper, five types of tissues, human enamel, human cortical bone, human trabecular bone, muscular tissue, and fatty tissue were imaged ex vivo using optical coherence tomography (OCT). The specimens were prepared in blocks of 5 x 5 x 3 mm (width x length x height). The OCT imaging system was a swept source OCT system operating at wavelengths ranging between 1250 nm and 1360 nm with an average power of 18 mW and a scan rate of 50 to 100 kHz. The imaging probe was placed on top of a 2 x 2 cm stabilizing device to maintain a standard distance from the samples. Ten image samples from each type of tissue were obtained. To acquire images with minimum inhomogeneity, imaging was performed multiple times at different points. Based on the observed texture differences between OCT images of soft and hard tissues, spatial and spectral features were quantitatively extracted from the OCT images. The Radon transform from angles of 0 deg to 90 deg was computed, averaged over all the angles, normalized to peak at unity, and then fitted with Gaussian function. The mean absolute values of the spatial frequency components of the OCT image were considered as a feature, where 2-D fast Fourier transform (FFT) was done to OCT images. These OCT features can reliably differentiate between a range of hard and soft tissues, and could be extremely valuable in assisting dentists for in vivo evaluation of oral tissues and early detection of pathologic changes in tissues.
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