Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph.

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

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.

Electrophoretic mobility shift assay on poly(ethylene glycol)-modified glass microchips for the study of estrogen responsive element binding.

The binding of estrogen receptor (ER) to estrogen response element (ERE) is essential for genomic pathways of estrogens and gel-based electrophoretic mobility shift assay (EMSA) is commonly used for analyzing ERE binding. Gel-based EMSA, however, requires the use of hazard radio isotopes and they are slow, labor-intensive and difficult to quantify. Here, we present quantitative affinity assays based on microchip electrophoresis using PEG-modified glass microchannels, which bear neutral surfaces against the adsorption of acidic DNA molecules and basic ER proteins. We first demonstrated the feasibility of the method by measuring binding constants of recombinant ERalpha and ERbeta with a consensus ERE sequence (cERE, 5'-GGTCAGAGTGACC-3') as well as with an ERE-like sequence (ERE 1576, 5'-GACCGGTCAGCGGACTCAC-3'). Changes in mobility as a function of protein-DNA molar ratios were plotted and the dissociation constants were determined based on non-linear curve fitting. The minimum amount of ER proteins required for one assay was around 0.2 ng and the run time for one chip analysis was less than 2 min. We further measured the estrogenic compound-mediated dissociation constants with recombinant ER proteins as well as with the extracted ERbeta from treated and untreated A549 bronchioloalveolar carcinoma cells. Dissociation constants determined by this method agree with the fact that agonist compounds such as 17beta-estradiol (1.70 nM), diethylstilbestrol (0.14 nM), and genistein (0.80 nM) assist ERE binding by decreasing the constants; while antagonist compounds such as testosterone (140.4 nM) and 4-hydroxytamoxifen (10.5 nM) suppress the binding by increasing the dissociation constant.

Poly(dimethylsiloxane)-based microfluidic device with electrospray ionization-mass spectrometry interface for protein identification.

An easy method to fabricate poly(dimethylsiloxane) (PDMS)-based microfluidic chips for protein identification by tandem mass spectrometry is presented. This microchip has typical electrophoretic microchannels, a flow-through sampling inlet, and a sheathless nanoelectrospray ionization (ESI) interface. The surface of the microchannel was modified with 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS) and the generated electroosmotic flow under acidic buffer condition used for the separation was found to be more stable compared to that generated by the microchannel without modification. The feasibility of the device for flow-through sampling, separation, and ESI-MS/MS analysis was demonstrated by the analysis of a standard mixture composed of three tryptic peptides. Results show that four peaks corresponding to three peptide standards and acetylated products of the standard peptide were well resolved and the deduced sequences were consistent with those expected. Furthermore, the compatibility of this device with other miniaturized devices to integrate the whole process was also explored by connecting a miniaturized enzymatic digestion cartridge and a desalting cartridge in series to the sampling inlet of the microchip for the identification of a model protein, beta-casein.