Pneumonia is an acute respiratory infection, posing a serious threat to health and lives. Lung ultrasound (LUS), as a non-invasive and rapid imaging technique, can monitor real-time changes in lung, providing valuable assistance in clinical diagnosis. However, most LUS studies are limited to frame-level analysis and ignore respiratory cycle changes, leading to diagnostic errors. To address these problems, we propose a cross-dimensional spatial-temporal feature integration model for LUS video analysis. Specifically, the sliding window and feature difference analysis are first utilized to preprocess the original LUS videos for eliminating invalid and highly similar frames and implementing abstract video. Subsequently, a cross-dimensional feature fusion backbone integrates an improved temporal-C3D network and a self-designed recursive inception-meet-transformer (IMT) network to extract features from different dimensions for fusion. Thereby, comprehensive features can be obtained for characterizing LUS videos. Finally, the Longformer is employed to analyze the temporal dependencies of cross-dimensional features, supplemented by a classification head for evaluating LUS videos. 3018 LUS video clips were collected from 119 patients in three hospitals for the evaluation of the proposed LUS video scoring model. By dividing at the patient level, the training and testing set consist of 2652 clips from 104 patients and 366 clips from 15 patients, respectively. Experimental results of 5-fold cross validation demonstrate that the proposed model achieves outstanding scoring performance, with an accuracy, precision, recall, specificity, F1-score, and AUC of $91.78~\pm ~0.52$ %, $92.19~\pm ~0.76$ %, $91.81~\pm ~0.62$ %, $97.17~\pm ~0.20$ %, $91.94~\pm ~0.44$ %, and $97.83~\pm ~0.19$ %, respectively. The independent testing set also shows the superior generalization capability with a scoring accuracy of $87.65~\pm ~1.12$ %. Moreover, ablation studies confirm that each designed module contributes significantly to the model's performance, and comparative experiments further confirm the superiority of the proposed model compared to previous models. These robust findings highlight the proposed LUS video scoring model's strong potential for clinical deployment.

Early prognostic prediction is crucial for determining appropriate clinical interventions. Previous single-omics models had limitations, such as high contingency and overlooking complex physical conditions. In this paper, we introduced multi-omics graph knowledge representation to predict in-hospital outcomes for pneumonia patients. This method utilizes CT imaging and three non-imaging omics information, and explores a knowledge graph for modeling multi-omics relations to enhance the overall information representation. For imaging omics, a multichannel pyramidal recursive MLP and Longformer-based 3D deep learning module was developed to extract depth features in lung window, while radiomics features were simultaneously extracted in both lung and mediastinal windows. Non-imaging omics involved the adoption of laboratory, microbial, and clinical indices to complement the patient's physical condition. Following feature screening, the similarity fusion network and graph convolutional network (GCN) were employed to determine omics similarity and provide prognostic prediction. The results of comparative experiments and generalization validation demonstrat that the proposed multi-omics GCN-based prediction model has good robustness and outperformed previous single-type omics, classical machine learning, and previous deep learning methods. Thus, the proposed multi-omics graph knowledge representation model enhances early prognostic prediction performance in pneumonia, facilitating a comprehensive assessment of disease severity and timely intervention for high-risk patients.

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease with a high mortality rate. Early diagnosis of risk grading is of great significance for clinical guidance and treatment. In this paper, we propose an automated COPD staging diagnosis model by combining deep/machine learning and computed tomography (CT) scan analysis. This model is composed of three parts: lung parenchyma segmentation, imaging feature analysis, and classification. Firstly, the fast Fourier transform (FFT)-guided dual branch Mamba model is proposed achieve accurate lung parenchyma segmentation in 2-dimensional CT slice images, in which FFT can improve the model’s performance to capture edge and texture information. Next, the Radiomics is employed to analysis the features of segmented 3-dimensional lung parenchyma in CT scans. After effective feature selection, the machine learning classifiers (i.e., K-Nearest Neighbor, Linear Discriminant Analysis, and Support Vector Machine) are used to achieve staged diagnosis of patients with COPD. The experimental results demonstrate that the lung parenchyma segmentation model proposed in this paper has good segmentation performance in CT slice images, with IoU of 0.9819 and Dice of 0.9908. Meanwhile, through 5-fold cross validation, the support vector machine classifier can obtain the best classification performance for COPD staging diagnosis, with the accuracy of 88.57%. The above results prove that the model proposed in this paper has superior diagnosis performance and clinical application potential in small sample situations.

Multilayered composite media are widely used in various industries, and the presence of small defects like voids or pores could lead to reduced mechanical properties. Ultrasound imaging with full-matrix capture (FMC) is a well-established modality to detect the small defect. However, the sequential emission of probe element combined with full-matrix reception results in heavy computational complexity and low frame rates, limiting real-time implementation. Furthermore, conventional FMC methods are only suitable for single-layer media and will be inaccurate for multilayered structures. To overcome these limitations, an efficient approach called spectrum-domain plane wave imaging (SD-PWI) was proposed to imaging multilayered media. By modifying the exploding reflector model to be applicable to PWI in multilayered imaging scenarios, the received wavefield was accurately extrapolated to the top of the objective layer, and the entire layer of interest was successfully reconstructed, employing fast Fourier transform based beamforming. Experimental findings demonstrated the effectiveness of SD-PWI. Compared with two classical FMC approaches, such as ray-tracing synthetic aperture and extended phase shift migration, multiangle compounded SD-PWI achieved improved image quality and higher efficiency. The side-drilled holes with diameters of 1–2.5 mm can be effectively detected, showcasing its ability to diagnose minor defects. Moreover, SD-PWI achieved a frame rate of 15 Hz for 3-layer medium imaging using a 192-element phased array. It is demonstrated that the proposed SD-PWI method is an accurate and efficient modality to studying multilayered media in industrial applications.

Pneumonia is a contagious disease that poses a great threat to human health. The real-time and free-radiation of lung ultrasound (LUS) makes it an essential tool for diagnosing pneumonia. This paper aims to explore an automatic detection model based on lung ultrasound imaging, called IMT-LUSS, which can achieve effective lung scoring. The model combines inception with transformer, intends to incorporate multi-scale information while considering global information. Firstly, the input is compressed using a stem block. Then it is incorporated into the multi-scale inception meet transformer (IMT) block for information representation, which includes a flexible inception module composed of three convolutional branches with different receptive fields and a pooling branch, and a feature encoding module improved by the multi-head self-attention mechanism with depth-wise convolution. Finally, automatic scoring of LUS images is completed based on feature representation information. 19330 LUS images were employed to verify the proposed IMTLUSS model. Experimental results demonstrate that this model has a great LUS scoring performance with high accuracy of 99.44 ± 0.14%. Meanwhile, the ablation experiments on structure and comparative experiments with other models proved its significant superiority, indicating the potential in future clinical applications.