Background/Objectives: The integration of digital imaging technologies in dentistry has revolutionized diagnostic and treatment practices, with panoramic radiographs playing a crucial role in detecting impacted teeth. Manual interpretation of these images is time consuming and error prone, highlighting the need for automated, accurate solutions. This study proposes an artificial intelligence (AI)-based model for detecting impacted teeth in panoramic radiographs, aiming to enhance accuracy and reliability. Methods: The proposed model combines YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) models to leverage their strengths in real-time object detection and learning long-range dependencies, respectively. The integration is further optimized with the Weighted Boxes Fusion (WBF) algorithm, where WBF parameters are tuned using Bayesian optimization. A dataset of 407 labeled panoramic radiographs was used to evaluate the model’s performance. Results: The model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%, significantly outperforming individual models and other combinations. The results were expressed through key performance metrics, such as mAP and F1 scores, which highlight the model’s balance between precision and recall. Visual and numerical analyses demonstrated superior performance, with enhanced sensitivity and minimized false positive rates. Conclusions: This study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs, offering substantial improvements in diagnostic accuracy and efficiency. The proposed model has potential for widespread application in clinical dentistry, reducing manual workload and error rates. Future research will focus on expanding the dataset and further refining the model’s generalizability.

Detection of Mental Foramen in Panoramic Radiographs with Artificial Intelligence-based Systems - Journal of Health Institutes of Türkiye

This study aims to develop an artificial intelligence-based classification model for early detection of plant diseases that cause yield loss in hazelnut production in Turkey. Apple mosaic virus, Armillaria root rot, hazelnut bacterial blight, powdery mildew and Ganoderma root rot diseases were analyzed with images obtained from the Black Sea Region. The proposed model is based on ResNet-50 architecture augmented with Squeeze-and-Excitation (SE) blocks, including multi-scale feature extraction and channel-based attention mechanism. Evaluated with 10-fold cross-validation, the model achieved the highest success with 89.53% accuracy and 89.31% F1 score. The results show that AI-based early detection systems have significant potential in disease detection and improving agricultural productivity.

Lunar surface landing and terrain navigation is an essential component of space exploration missions. In such missions, accurate detection of craters is critical for a safe landing and efficient navigation. To this end, this study focuses on the problem of crater detection in lunar surface images acquired at different distances and resolutions. Three different datasets consisting of images obtained at 5 $\mathrm{km}, 10 \mathrm{~km}$ and 20 km scales were used in the experiments. YOLO and DETR architectures for crater detection are investigated and their performance is compared. The results show that the models successfully detect craters with mAP50 scores of $80 \%$ and above. In this context, it is concluded that the proposed models can help the spacecraft to successfully detect and avoid craters at every stage of its approach to the lunar surface. This study sheds light on the development of crater detection techniques for future space exploration missions and lunar surface landing projects.

Background/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment.