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Background: The patellar height index is important; however, the measurement procedures are time-consuming and prone to significant variability among and within observers. We developed a deep learning-based automatic measurement system for the patellar height and evaluated its performance and generalization ability to accurately measure the patellar height index. Methods: We developed a dataset containing 3,923 lateral knee X-ray images. Notably, all X-ray images were from three tertiary level A hospitals, and 2,341 cases were included in the analysis after screening. By manually labeling key points, the model was trained using the residual network (ResNet) and high-resolution network (HRNet) for human pose estimation architectures to measure the patellar height index. Various data enhancement techniques were used to enhance the robustness of the model. The root mean square error (RMSE), object keypoint similarity (OKS), and percentage of correct keypoint (PCK) metrics were used to evaluate the training results. In addition, we used the intraclass correlation coefficient (ICC) to assess the consistency between manual and automatic measurements. Results: The HRNet model performed excellently in keypoint detection tasks by comparing different deep learning models. Furthermore, the pose_hrnet_w48 model was particularly outstanding in the RMSE, OKS, and PCK metrics, and the Insall–Salvati index (ISI) automatically calculated by this model was also highly consistent with the manual measurements (intraclass correlation coefficient [ICC], 0.809–0.885). This evidence demonstrates the accuracy and generalizability of this deep learning system in practical applications. Conclusion: We successfully developed a deep learning-based automatic measurement system for the patellar height. The system demonstrated accuracy comparable to that of experienced radiologists and a strong generalizability across different datasets. It provides an essential tool for assessing and treating knee diseases early and monitoring and rehabilitation after knee surgery. Due to the potential bias in the selection of datasets in this study, different datasets should be examined in the future to optimize the model so that it can be reliably applied in clinical practice. Trial registration: The study was registered at the Medical Research Registration and Filing Information System (medicalresearch.org.cn) MR-61-23-013065. Date of registration: May 04, 2023 (retrospectively registered). [ABSTRACT FROM AUTHOR]

High-resolution geological mapping is an important supporting condition for mineral and energy exploration. However, high-resolution geological mapping work still faces many problems. At present, high-resolution geological mapping is still generated by expert interpretation of survey lines, compasses, and field data. The work in the field is constrained by the weather, terrain, and personnel, and the working methods need to be improved. This paper proposes a new method for high-resolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect high-resolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on high-resolution remote sensing images. This method transfers a large amount of field work into the room and provides an automatic mapping process based on the Simple Linear Iterative Clustering-Convolutional Neural Network (SLIC-CNN) algorithm. It uses the convolutional neural network (CNN) to identify the image content and confirms the lithologic distribution, the simple linear iterative cluster (SLIC) algorithm can be used to outline the boundary of the rock mass and determine the contact interface of the rock mass, and the mode and expert decision method is used to clarify the results of the fusion and mapping. The mapping method was applied to the Taili waterfront in Xingcheng City, Liaoning Province, China. In this study, the Area Under the Curve (AUC) of the mapping method was 0.937. The Kappa test result was k = 0.8523, and a high-resolution geological map was obtained. [ABSTRACT FROM AUTHOR]

The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field. [ABSTRACT FROM AUTHOR]