Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).

Objective. To review the progress of research on photoplethysmography‐ (PPG‐) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

Automatic generation of chest X-ray reports, designed to produce clinically precise descriptions from chest X-ray images, is gaining significant research attention because of its vast potential in clinical applications. Recently, despite considerable progress, current models typically adhere to a CNN–Transformer-based framework, which still fails to enhance the perceptual field during image feature extraction. To solve this problem, we propose the Reinforced Memory-driven Pure Transformer (RMPT), which is a novel Transformer–Transformer-based model. In implementation, our RMPT employs the Swin Transformer to extract visual features from given X-ray images, which has a larger perceptual field to better model the relationships between different regions. Furthermore, we adopt a memory-driven Transformer (MemTrans) to effectively model similar patterns in different reports, which is able to facilitate the model to generate long reports. Finally, we present an innovative training approach leveraging Reinforcement Learning (RL) that efficiently steers the model to focus on challenging samples, consequently improving its comprehensive performance across both straightforward and complex situations. Experimental results on the IU X-ray dataset show that our proposed RMPT achieves superior performance on various Natural Language Generation (NLG) evaluation metrics. Further ablation study results demonstrate that our RMPT model achieves 10.5% overall performance compared to the base mode.

Purpose: The livestock industry is undergoing a critical transition to intensive, large-scale farming. Intelligent monitoring technologies are essential for improving epidemic early warning systems, reducing breeding costs, and promoting sustainable production. This study aimed to develop a novel pig behavior recognition method using advanced computer vision technology to support intelligent livestock farming.Design/methodology/approach: The YOLOv5 model was utilized to achieve contactless and efficient monitoring of daily pig activities. The study enhanced the YOLOv5 model by improving its input mechanism, backbone network and by incorporating the shuffle attention module. These modifications significantly improved the ability of the model to capture and interpret the spatiotemporal features of pig behavior.Findings: The experimental results demonstrate that compared with the original YOLOv5 model, the Precision, Recall, mAP@0.5 and mAp@0.5:0.95 of the proposed model has improved by 3.0%, 2.3%, 2.6% and 10.5%, respectively. These findings showcase the model’s effectiveness and potential for real-world applications in intelligent livestock farming, and highlight the feasibility of employing advanced computer vision models to enhance monitoring and management in animal farming environments.Originality/value: This study presents a novel approach to pig behavior recognition by integrating cutting-edge computer vision techniques with YOLOv5 enhancements. This study contributes to the field by addressing the challenges of spatiotemporal feature extraction and demonstrating the practical application of these methods in intelligent livestock farming. Future research directions include generalization to other animal species, integration with other sensor data, teal-time monitoring and decision support and application in wildlife and laboratory animal research, thus further advancing the intelligent breeding industry.