Intelligent medicine最新文献

Background

Breast cancer has the highest prevalence among all cancers in women globally. The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern. In computer-aided diagnosis, most traditional classification models use a single network to extract features, although this approach has significant limitations. Moreover, many networks are trained and optimized on patient-level datasets, ignoring lower-level data labels.

Methods

This paper proposed a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions. First, the BreaKHis dataset was randomly divided into training, validation, and test sets. Then, data augmentation techniques were used to balance the numbers of benign and malignant samples. Third, based on their transfer learning performance and the complementarity between networks, VGG16, Xception, ResNet50, and DenseNet201 were selected as base classifiers.

Results

In a ensemble network model with accuracy as the weight, the image-level binary classification achieved an accuracy of $98.90\%$. To verify the capabilities of our method, it was experimentally compared with the latest transformer and multilayer perception (MLP) models on the same dataset. Our ensemble model showed a $5\%$–$20\%$ advantage, emphasizing its far-reaching abilities in classification tasks.

Conclusions

This research focuses on improving the performance of a classification model with an ensemble algorithm. Transfer learning has an essential role in classification of small datasets, improving training speed and accuracy. Our model may outperform many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.

After the outbreak of COVID-19, the interaction of infectious disease systems and social systems has challenged traditional infectious disease modeling methods. Starting from the research purpose and data, researchers improved the structure and data of the compartment model or used agents and artificial intelligence based models to solve epidemiological problems. In terms of modeling methods, the researchers use compartment subdivision, dynamic parameters, agent-based model methods, and artificial intelligence related methods. In terms of factors studied, the researchers studied 6 categories: human mobility, nonpharmaceutical interventions (NPIs), ages, medical resources, human response, and vaccine. The researchers completed the study of factors through modeling methods to quantitatively analyze the impact of social systems and put forward their suggestions for the future transmission status of infectious diseases and prevention and control strategies. This review started with a research structure of research purpose, factor, data, model, and conclusion. Focusing on the post-COVID-19 infectious disease prediction simulation research, this study summarized various improvement methods and analyzes matching improvements for various specific research purposes.

Medical artificial intelligence (AI) and big data technology have rapidly advanced in recent years, and they are now routinely used for image-based diagnosis. China has a massive amount of medical data. However, a uniform criteria for medical data quality have yet to be established. Therefore, this review aimed to develop a standardized and detailed set of quality criteria for medical data collection, storage, annotation, and management related to medical AI. This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

Background

Ankylosing spondylitis (AS) generally occurs in young adults. The functional impairments resulting in limitation in activities and social participation might exert lifetime impacts. The present study investigated the preferences for mobile health (mHealth) features motivating the self-management behaviors in AS.

Methods

The present study was an internet-based, nationwide quantitative study based on the Chinese Ankylosing Spondylitis Prospective Imaging Cohort (CASPIC) study, which was a nationwide, ongoing, prospective cohort study launched in conjunction with Smart-phone SpondyloArthritis Management System (SpAMS) in China. Participants with AS from the CASPIC were invited to report their mHealth preferences from December 2019 to February 2020. The questionnaire was designed to determine the patient preferences for 28 mHealth features. Sociodemographic characteristics, including age, gender, and work status, were collected.

Results

Among all the visitors to the online questionnaire (n = 872), 93.9% (819/872) respondents fully completed the questionnaire and were enrolled in the present study. The mean age was (33.4 ± 9.0) years, and 70.57% (578/819) of the respondents were males. The mean scores of 22 (78.57%) features were greater than 4 (like or strongly like). The mean standard deviation (SD) score of exercise instructions was 4.70 (0.63), which was the most preferred feature, whereas the social interaction features were preferred the least. Pain analysis was more preferred among female respondents (4.72 vs. 4.60, P = 0.012), whereas all items of the social interaction theme and social interaction as a whole (3.73 vs. 3.52, P < 0.001) were less preferred among female respondents. Additionally, the following themes were more preferred by respondents aged ≤ 40 years: credibility and styling (4.37 vs. 4.19, P < 0.001); disease action support (4.55 vs. 4.47, P = 0.007); and incentivization (4.35 vs. 4.24, P = 0.025).

Conclusion

AS patients show great interest for the majority of mHealth features. Exercise instructions and exercise scheduling are the most preferred features, whereas social interaction is the least preferred feature. In addition, gender-related and age-related differences exist in mHealth feature preferences.

International movement plays an important role in spatial spread of infectious diseases. Here, we share two successful COVID-19 interventions based on real-time digital information collected from international passengers, which have been performed in Greece and China respectively. Both of the interventions demonstrated good performance and showed the potential of real-time digital data in containing the spread. However, several key points should not be ignored when we promote similar strategies.

Faced with the current time-sensitive COVID-19 pandemic, the overburdened healthcare systems have resulted in a strong demand to develop newer methods to control the spread of the pandemic. Big data and artificial intelligence (AI) have been leveraged amid the COVID-19 pandemic; however, little is known about their use for supporting public health efforts. In epidemic surveillance and containment, efforts are needed to treat critical patients, track and manage the health status of residents, isolate suspected cases, and develop vaccines and antiviral drugs. The applications of emerging practices of artificial intelligence and big data have become powerful “weapons” to fight against the pandemic and provide strong support in pandemic prevention and control, such as early warning, analysis and judgment, interruption and intervention of epidemic, to achieve goals of early detection, early report, early diagnosis, early isolation and early treatment. These are the decisive factors to control the spread of the epidemic and reduce the mortality. This paper systematically summarized the application of big data and AI in epidemic, and describes practical cases and challenges with emphasis on epidemic prevention and control. The included studies showed that big data and AI have the potential strength to fight against COVID-19. However, many of the proposed methods are not yet widely accepted. Thus, the most rewarding research would be on methods that promise value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for practice.

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision. In the field of medical image analysis, transformers have also been successfully used in to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. This paper aimed to promote awareness of the applications of transformers in medical image analysis. Specifically, we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic components. Second, we reviewed various transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigated key challenges including the use of transformers in different learning paradigms, improving model efficiency, and coupling with other techniques. We hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

Background

The coronavirus disease 2019 (COVID-19) pandemic is ravaging the world. Many therapies have been explored to treat COVID-19. This report aimed to assess the global research trends for the development of COVID-19 therapies.

Methods

We searched the relevant articles on COVID-19 therapies published from January 1, 2020, to May 25, 2022, in the Web of Science Core Collection Database (WOSCC). VOSviewer 1.6.18 software was used to assess data on the countries, institutions, authors, collaborations, keywords, and journals that were most implicated in COVID-19 pharmacological research. The latest research and changing trends in COVID-19-relevant pharmacological research were analyzed.

Results

After manually eliminating articles that do not meet the requirements, a total of 5,289 studies authored by 32,932 researchers were eventually included in the analyses, which comprised 95 randomized controlled trials. 3,044 (57.6%) studies were published in 2021. The USA conducted the greatest number of studies, followed by China and India. The primary USA collaborators were China and England. The topics covered in the publications included: the general characteristics, the impact on pharmacists’ work, the pharmacological research, broad-spectrum antiviral drug therapy and research, and promising targets or preventive measures, such as vaccine. The temporal diagram revealed that the current research hotspots focused on the vaccine, molecular docking, Mpro, and drug delivery keywords.

Conclusion

Comprehensive bibliometric analysis could aid the rapid identification of the principal research topics, potential collaborators, and the direction of future research. Pharmacological research is critical for the development of therapeutic and preventive COVID-19-associated measures. This study may therefore provide valuable information for eradicating COVID-19.

Deep learning approaches, especially convolutional neural networks (CNNs), have become the method of choice in the field of medical image analysis over the last few years. This prevalence is attributed to their excellent abilities to learn features in a more effective and efficient manner, not only for 2D/3D images in the Euclidean space, but also for meshes and graphs in non-Euclidean space such as cortical surfaces in neuroimaging analysis field. The brain cerebral cortex is a highly convoluted and thin sheet of gray matter (GM) that is thus typically represented by triangular surface meshes with an intrinsic spherical topology for each hemisphere. Accordingly, novel tailored deep learning methods have been developed for cortical surface-based analysis of neuroimaging data. This paper reviewsed the representative deep learning techniques relevant to cortical surface-based analysis and summarizes recent major contributions to the field. Specifically, we surveyed the use of deep learning techniques for cortical surface reconstruction, registration, parcellation, prediction, and other applications. We concluded by discussing the open challenges, limitations, and potentials of these techniques, and suggested directions for future research.