The dental panoramic radiograph (DPR) is a pivotal diagnostic tool in dentistry. However, despite the growing prevalence of artificial intelligence (AI) across various medical domains, manual methods remain the prevailing means of interpreting DPR images. This study aims to introduce an advanced identification system for detecting seven dental conditions in DPR images by utilizing Faster R-CNN. The primary objectives are to enhance dentists’ efficiency and evaluate the performance of various CNN models as foundational training networks. This study contributes significantly to the field in several notable ways. Firstly, including a Butterworth filter in the training process yielded an approximately 7% enhancement in judgment accuracy. Secondly, the proposed enhancement technology tailored to different dental symptoms effectively bolstered the training model’s accuracy. Consequently, all dental conditions attained an accuracy rate exceeding 95% in CNN analysis. These accuracy enhancements ranged from 1.34% to 13.24% compared to existing recognition technologies. Thirdly, this study pioneers the application of Faster R-CNN for identifying dental conditions, achieving an impressive accuracy rate of 94.18%. The outcomes of this study mark a substantial advancement compared to prior research and offer dentists a more efficient and convenient means of pre-diagnosing dental conditions.

Thermographic imaging has gained significant use in recent years, particularly during the epidemic, including its application in architecture for damage detection on archaeological monuments through temperature analysis. The non-invasive nature of thermographic imaging, along with its ability to visualize temperature levels, allows for problem identification while preserving the building’s structure. The integration of artificial intelligence further enhances its potential applications. This study aims to propose an automated inspection system using a convolutional neural network (CNN) for analyzing abnormal floor blocks and its materials. A team of academicians with more than seven years of expertise in monument preservation gathered the imaging data for this investigation. They were in charge of collecting thermal imaging photos of floors at significant monuments and aiding in the identification of overheating data and floor tile types. This study will propose three types of CNN models for recognition: one for identifying floors in visible images, one for detecting abnormal temperatures in thermal images, and one for recognizing materials in visible images. The block with abnormal temperature radiations can be determined from the floor by analyzing elevated temperatures. Subsequently, analyzing materials in abnormal block can efficiently identify problematic materials. The identification accuracy rate of this study is as high as 99.16%. Compared to the efficiency of professionals identifying 100 images, this research increases efficiency by approximately 99.92%, which is an amazing improvement. These functions increase the practicality of restoration efforts, improve restoration quality and efficiency, and contribute to academic research on ancient monument preservation.

Background/Objectives: Pleural effusion is a common pulmonary condition that, if left untreated, may lead to respiratory distress and severe complications. Chest X-ray (CXR) imaging is routinely used by physicians to identify signs of pleural effusion. However, manually examining large volumes of CXR images on a daily basis can require substantial time and effort. To address this issue, this study proposes an automated pleural effusion detection system for CXR images. Methods: The proposed system integrates image cropping, image enhancement, and the EfficientNet-B0 deep learning model to assist in detecting pleural effusion, a task that is often challenging due to subtle symptom presentation. Image cropping was applied to extract the region from the heart to the costophrenic angle as the target area. Subsequently, image enhancement techniques were employed to emphasize pleural effusion features, thereby improving the model’s learning efficiency. Finally, EfficientNet-B0 was used to train and classify pleural effusion cases based on processed images. Results: In the experimental results, the proposed image enhancement approach improved the model’s recognition accuracy by approximately 4.33% compared with the non-enhanced method, confirming that enhancement effectively supports subsequent model learning. Ultimately, the proposed system achieved an accuracy of 93.27%, representing a substantial improvement of 21.30% over the 77.00% reported in previous studies, highlighting its significant advancement in pleural effusion detection. Conclusions: This system can serve as an assistive diagnostic tool for physicians, providing standardized detection results, reducing the workload associated with manual interpretation, and improving the overall efficiency of pulmonary care.

Infrared thermography (IRT), a widely used nondestructive testing method, is commonly employed to identify moisture in historic walls. However, its reliance on manual interpretation by experts makes the process both time-consuming and costly. This study addresses the challenge of detecting wall moisture; this issue is closely linked to the deterioration of cultural heritage structures. This study focuses on the brick walls of the Tainan Confucian Temple, the oldest Confucian temple in Taiwan. The targeted are walls coated with lime plaster mixed with red mineral pigments, a traditional finish that gives the temple its distinctive red appearance. This study proposes a system to automatically identify wall areas, mark low-temperature zones, and determine the presence and distribution of moisture. Visible and infrared thermal images of these walls are captured and preprocessed to normalize the size and enhance the features. Finally, two convolutional neural network (CNN) models are trained in this study: one for identifying wall regions and the other for detecting low-temperature areas. The proposed method achieves an accuracy of 91.18% in detecting wall moisture, representing a 24.05% improvement over conventional object recognition techniques, the accuracy of which is 73.5%. In addition, this method requires only 3 s to detect the wall moisture, representing a 99.92% reduction in processing time compared to the conventional manual method. This method not only provides a fast and objective method for assessing moisture in lime-plastered heritage walls but also significantly enhances the efficiency of restoration efforts. This method can be applied to similar wall structures in other Confucian temples, offering broad potential for heritage conservation.

Background/Objectives: Panoramic radiography (PANO) is widely utilized for routine dental examinations, as a single PANO image captures most anatomical structures and clinical findings, enabling an initial assessment of overall dental health. Dentists rely on PANO images to enhance clinical diagnosis and inform treatment planning. With the advancement of artificial intelligence (AI), the integration of clinical data and AI-driven analysis presents significant potential for supporting medical applications. Methods: The proposed method focuses on the segmentation and localization of impacted third molars in PANO images, incorporating Sobel edge detection and enhancement methods to improve feature extraction. A convolutional neural network (CNN) was subsequently trained to develop an automated impacted tooth detection system. Results: Experimental results demonstrated that the trained CNN achieved an accuracy of 84.48% without image preprocessing and enhancement. Following the application of the proposed preprocessing and enhancement methods, the detection accuracy improved significantly to 98.66%. This substantial increase confirmed the effectiveness of the image preprocessing and enhancement strategies proposed in this study. Compared to existing methods, which achieve approximately 90% accuracy, the proposed approach represents a notable improvement. Furthermore, the entire process, from inputting a raw PANO image to completing the detection, takes only 4.4 s. Conclusions: This system serves as a clinical decision support system for dentists and medical professionals, allowing them to focus more effectively on patient care and treatment planning.

Background/Objectives: In dental medicine, the integration of various types of X-ray images, such as periapical (PA), bitewing (BW), and panoramic (PANO) radiographs, is crucial for comprehensive oral health assessment. These complementary imaging modalities provide diverse diagnostic perspectives and support the early detection of oral diseases, thereby enhancing treatment outcomes. However, there is currently no existing system that integrates multiple types of dental X-rays for both adults and children to perform tooth localization and numbering. Methods: Therefore, this study aimed to propose a system based on YOLOv8 that integrates multiple dental X-ray images and automatically detects and numbers both permanent and deciduous teeth. Through image preprocessing, various types of dental X-ray images were standardized and enhanced to improve the recognition accuracy of individual teeth. Results: With the implementation of a novel image preprocessing method, the system achieved a detection precision of 98.16% for permanent and deciduous teeth, representing a 3% improvement over models without image enhancement. In addition, the system attained an average tooth numbering accuracy of 98.5% for permanent teeth and 96.3% for deciduous teeth, surpassing existing methods by 5.6%. Conclusions: These results might highlight the innovation of the proposed image processing method and show its practical value in assisting clinicians with accurate diagnosis of tooth loss and the identification of missing teeth, ultimately contributing to improved diagnosis and treatment in dental care.