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Cardiovascular and cerebrovascular diseases are among the leading causes of death and disability worldwide. Despite continuous advancements in medical technology, the diagnosis and treatment of these diseases still face many unresolved challenges. The emergence of artificial intelligence and machine learning provides new approaches to addressing these issues. The American Heart Association’s 2024 scientific statement highlights the applications and challenges of artificial intelligence and machine learning in clinical scenarios, including imaging analysis, electrocardiogram interpretation, bedside monitoring, wearable devices, genomics, and electronic health records. This paper aims to introduce these aspects and analyze the advantages of artificial intelligence and machine learning in the field of cerebrovascular diseases, providing a reference for the future development of artificial intelligence and machine learning in this area in China. [ABSTRACT FROM AUTHOR]

Osteoporosis (OP) is a kind of bone disease related to aging. Its onset is obscure and development is gradual. Patients are without obvious clinical manifestations in the early stage. However, with the progress of the disease, bone loss and bone tissue microstructure destruction occur, and bone pain, spine deformation, and even osteoporotic fracture (OPF) and other serious complications may develop. Dual-energy X-ray absorption method is the current gold standard for clinical diagnosis of OP. Because its diagnostic accuracy is affected by the factors of body weight, lumbar degenerative changes, and aortic wall calcification, a false negative diagnosis is possible. In recent years, artificial intelligence (AI) has developed rapidly in the medical field. At present, AI has been widely used in the research of OP. Its research in the field of OP screening, diagnosis, and prediction has become a new hot spot. This paper expounds the application status and advantages of AI in OP diagnosis and treatment from four aspects, i.e., OP early screening, medical imaging performance, clinical diagnosis and treatment data, and OPF risk prediction, so as to provide a new direction for the accurate diagnosis and treatment of OP. [ABSTRACT FROM AUTHOR]

The production and sales of high-quality fruits are inseparable from fruit quality testing, while traditional fruit quality testing methods have low precision, high cost, poor timeliness, and strong destructiveness. With the continuous advancement of science and technology in recent years, low-cost and high-efficiency non-destructive testing technology for fruit quality has been developed rapidly. Among them, hyperspectral imaging technology has gradually become a research hotspot. This paper summarizes the technical principle, application and development status of this technology in the field of fruit quality, and discusses its performance potential, existing problems, development trend and application prospects in the field of non-destructive testing of fruit quality. On the whole, hyperspectral imaging technology can non-destructively and efficiently realize the effective detection of various fruits and various quality indicators, such as maturity, sugar content, acidity, red index, etc.; Limited by hardware technology, its development focuses on data mining, that is, in the case of limited hardware development, accurate analysis results can be obtained through continuously updated and optimized targeted algorithms; on the other hand, expensive equipment, complex data processing, and poor model universality are the main problems that this technology needs to be further optimized and improved; Its future development will be based on efficient data processing of cloud computing and artificial intelligence, research and development of hyperspectral detection equipment for fruit quality with a wider range of applications, and multi-source comprehensive non-destructive testing. With the continuous development of technology, hyperspectral imaging technology has broad application prospects in non-destructive testing of fruit quality, and will become one of the important means of fruit quality testing in the future. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) and machine learning develops new pathways and new modalities for the diagnosis and treatment of hypertension. This paper reviews the progress of AI applied in aspects of hypertension prediction, assessment, and assistance for treatment decision-making. In terms of hypertension prediction, AI could build hypertension risk prediction models based on traditional hypertension risk factors combined with imaging, genomics and other factors, which improves the early diagnosis rate of hypertension. The AI expert system identifies complex etiology of hypertension, which makes up for the limitations of clinicians in diagnosis. In terms of hypertension assessment, the prediction model of hypertension complication constructed by AI could predict complications in the heart and the brain blood vessel, and screen out high-risk population for early intervention. In terms of hypertension treatment, the application of clinical decision-making model, drug factor analysis model, monitoring and early warning system, human-computer interaction, etc. could improve the accuracy, timeliness and patient compliance of hypertension treatment, and assist clinicians in making optimal treatment decisions. The exploration of AI in the field of hypertension diagnosis and treatment has established a new model for the chronic disease management of hypertension. [ABSTRACT FROM AUTHOR]

Ocean mesoscale eddy is an important ocean mesoscale phenomenon that plays an important role in ocean circulation, mat erial and energy transport, and has an important impact on the safety of ship navigation and hydroacoustic communication. Efficient and accurate detection and identification of ocean mesoscale eddies are of great research value for both physical ocean cognition and ocean exploitation. Traditional eddy detection and identification methods rely on a single threshold designed by experts experiences. With the rise of deep learning, the current machine learning methods show certain advantages in the accuracy and automation of eddy detection and identification. This paper summarizes and comparatively analyzes the existing machine learning-based detection and identification methods to provide a systematic knowledge and reference basis for the development of ocean mesoscale eddy detection and identification research. [ABSTRACT FROM AUTHOR]

In order to maintain security and reliability of operating system, this paper developed a universal framework for net-work intrusion detection system based on artificial intelligence( AI) model. This framework was capability of detecting all sorts of network traffic, catching out possible malicious connection or attack from Internet and identifying it. Firstly, this system pre-processed the network traffic data by sampling, one-hot encoding, feature selection and normalization for basic feature extrac-tion. Secondly,it utilized feature distribution in network connection instance to build a scoring mechanism for information ex-traction once again. Thirdly, it applied the different types of ML/DL models for different forms of network traffic to do final classification for extracted features. In experiments, this system applied three benchmark datasets, KDDCup99, UNSW-NB15 and CICIDS2017. The result of experiments shows it possesses exceedingly good performance and has better accuracy and F1 score than related works under these datasets. [ABSTRACT FROM AUTHOR]

Objective: Develop a machine learning model, and to evaluate its accuracy in the diagnosis of primary bone tumor around the knee joint. Methods: In this paper, the deep learning method of deep convolutional neural network（DCNN） was applied to the imagomics analysis of knee X-ray images, its clinical value in assisting the diagnosis of primary bone tumor around the knee joint were explored. Results: The deep learning model showed excellent diagnostic accuracy in differentiating normal images from tumor images.The overall accuracy of 5 rounds of testing using the DCNN model was（99.8±0.4） %, while the positive predictive value and negative predictive value were（100.0±0.0） % and（99.6±0.8） % respectively. The area under the curve（AUC） of each dataset was 0.99, 1.00,1.00, 1.0 and 1.0 respectively, average AUC was（0.998±0.004). A further 10 rounds of testing using the DCNN model showed an overall accuracy of（71.2±1.6） % in differentiating benign from malignant bone tumors, and a strong positive predictive value（91.9±8.5） %was achieved in differentiating benign and malignant bone tumor, the AUC of each dataset was 0.63, 0.63, 0.58, 0.69, 0.55, 0.63, 0.54,0.57, 0.73, 0.63 respectively, average AUC was（0.62±0.06). Conclusion: This is the first study on the application of artificial intelligence technology in the imaging analysis of X-ray images for bone tumor diagnosis, Artificial intelligence imaging models can help doctors to automatically and quickly screen bone tumors and identify benign or malignant tumors, with a high positive predictive value. MoreReset [ABSTRACT FROM AUTHOR]