This Letter reports on the first results from the Taiwan Axion Search Experiment with a Haloscope, a search for axions using a microwave cavity at frequencies between 4.707 50 and 4.798 15 GHz. Apart from the nonaxion signals, no candidates with a significance of more than 3.355 were found. The experiment excludes models with the axion-two-photon coupling $|g_{a\gamma \gamma }|\gtrsim 8.1\times {10}^{-14}{\mathrm{GeV}}^{-1}$, a factor of eleven above the benchmark Kim-Shifman-Vainshtein-Zakharov model, in the mass range $19.4687<m_a<19.8436\mu \mathrm{eV}$. It is also the first time that a haloscope experiment places constraints on $g_{a\gamma \gamma }$ in the mass region of $19.4687<m_a<19.7639\mu \mathrm{eV}$, reaching a sensitivity 3 orders of magnitude better than the limits obtained by nonhaloscope experiments.

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 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.

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification models are constructed based on one-dimensional convolutional neural network (1D-CNN) combined with a priority model integrated voting method to optimize the integrated classification effect. The experiments followed the recommended inter-patient scheme of the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, and the practicability and effectiveness of THML ECG data are proved with ablation experiments. Results show that the average accuracy of the N, V, S, F, and Q classes is 94.82%, 98.10%, 97.28%, 98.70%, and 99.97%, respectively, with the positive predictive value of the N, V, S, and F classes being 97.0%, 90.5%, 71.9%, and 80.4%, respectively. Compared with current studies, the THML ECG data can effectively improve the morphological integrity and time continuity of ECG information and the 1D-CNN model of ECG sequence has a higher accuracy for arrhythmia classification. The proposed method alleviates the problem of insufficient samples, meets the needs of medical ECG interpretation and contributes to the intelligent dynamic research of cardiac disease.

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0.

For daily dental practice, Panoramic (PANO) X-ray film is one of the most commonly used dental X-rays. One of its important advantages is coverage of most anatomic structures and clinical findings in a single film. The important information about clinical treatment and diagnosis can be provided from the expert analysis of the PANO. Combined with the assistance of artificial intelligence, the application has great potential. The purpose of this study was to propose an automated detection system based on several modern convolutional neural networks (CNN) for the classification of retained roots, endodontic treated teeth, and implants. In order to meet the standards of practical clinical application, the database used in this study is provided by dentists with more than three years of practical experience. The contributions of this work are 1) Proposed the more advanced techniques for image segmentation and image position in dental radiographs; 2) A better image enhancement is proposed, which improves the accuracy of the five CNNs to more than 96%; 3) Combined with Fuzzy operation to achieve more powerful and accurate anomaly detection. The final result has an accuracy rate of up to 98.75%. It is about 20% higher than previous techniques. This research designed to identify and document each specific finding automatically could help dentists obtain an objective treatment evaluation, and provide dentists more precious clinical time for dental operations and communication with patients.