ABSTRACT
PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality. METHODS: We propose a transformer sequential feature encoding structure to integrate multi-level information from complete CT images, inspired by the clinical practice of using a sequence of cross-sectional slices for diagnosis. We incorporate position encoding and cross-level long-range information fusion modules into the feature extraction CNN network for cross-sectional slices, ensuring high-precision feature extraction. RESULTS: We conducted comprehensive experiments on a dataset of 124 patients, with respective sizes of 64, 20 and 40 for training, validation and testing. The results of ablation experiments and comparative experiments demonstrated the effectiveness of our approach. Our method outperforms existing state-of-the-art methods in the 3D CT image classification problem of distinguishing between lung infections and pulmonary lymphoma, achieving an accuracy of 0.875, AUC of 0.953 and F1 score of 0.889. CONCLUSION: The experiments verified that our proposed position-enhanced transformer-based sequential feature encoding model is capable of effectively performing high-precision feature extraction and contextual feature fusion in the lungs. It enhances the ability of a standalone CNN network or transformer to extract features, thereby improving the classification performance. The source code is accessible at https://github.com/imchuyu/PTSFE .
ABSTRACT
Simultaneous location and mapping (SLAM) technology is key in robot autonomous navigation. Most visual SLAM (VSLAM) algorithms for dynamic environments cannot achieve sufficient positioning accuracy and real-time performance simultaneously. When the dynamic object proportion is too high, the VSLAM algorithm will collapse. To solve the above problems, this paper proposes an indoor dynamic VSLAM algorithm called YDD-SLAM based on ORB-SLAM3, which introduces the YOLOv5 object detection algorithm and integrates deep information. Firstly, the objects detected by YOLOv5 are divided into eight subcategories according to their motion characteristics and depth values. Secondly, the depth ranges of the dynamic object and potentially dynamic object in the moving state in the scene are calculated. Simultaneously, the depth value of the feature point in the detection box is compared with that of the feature point in the detection box to determine whether the point is a dynamic feature point; if it is, the dynamic feature point is eliminated. Further, multiple feature point optimization strategies were developed for VSLAM in dynamic environments. A public data set and an actual dynamic scenario were used for testing. The accuracy of the proposed algorithm was significantly improved compared to that of ORB-SLAM3. This work provides a theoretical foundation for the practical application of a dynamic VSLAM algorithm.
ABSTRACT
Background and aim: Health literacy levels are strongly associated with clinical outcomes and quality of life in patients with chronic diseases, and patients with limited health literacy often require more medical care and achieve poorer clinical outcomes. Among the large number of studies on health literacy, few studies have focused on the health literacy of people with systemic sclerosis (SSc), and there is no specific tool to measure health literacy in this group. Therefore, this study plans to develop a health literacy scale for patients with SSc. Methods: This study included 428 SSc patients from the outpatient and inpatient departments of the Department of Rheumatology and Immunology, the first affiliated Hospital of Anhui Medical University and the first affiliated Hospital of University of Science and Technology of China. The formulation of the scale was completed by forming the concept of health literacy of SSc patients, establishing the item pool, screening items, and evaluating reliability and validity. Classical measurement theory was used to screen items, factor analysis was used to explore the construct validity of the scale, and Cronbach's alpha coefficient was used to assess the internal consistency. Results: Our study population was predominantly middle-aged women, with a male to female ratio of 1:5.7 and a mean age of 51.57 ± 10.99. A SSc Health Literacy scale with 6 dimensions and 30 items was developed. The six dimensions are clinic ability, judgment/evaluation information ability, access to information ability, social support, treatment compliance and application information ability. The Cronbach's alpha coefficient of the scale is 0.960, retest reliability is 0.898, split-half reliability is 0.953, content validity is 0.983, which has good reliability and validity. Conclusion: The Systemic Sclerosis Health Literacy Scale may become a valid tool to evaluate the health literacy level of patients with SSc.
