Intelligent medicine最新文献

Testicular two-dimensional ultrasound is a testing modality that is often used to evaluate azoospermia and other related diseases. With the continuous development of deep learning in recent years, the combination of deep learning and testicular ultrasound appears unstoppable despite a lack of relevant standards. One of the major problems associated with the digitization of ultrasound images is the uneven quality of data however, and a standardized data source and acquisition process has not yet been developed. Such a standard could fill the current gap, and establish acquisition criteria for ultrasound images of testes during the male reproductive period, including grayscale ultrasound, shear wave elastography, and contrast-enhanced ultrasound. By following these guidelines the quality of testicular ultrasound images would be improved and standardized, which would lay a solid foundation for the standardization of testicular ultrasound images, and assist automated evaluation of testicular spermatogenic function of whole testis in azoospermic males.

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R₀ > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

The successful control of chronic diseases mainly depends on how well patients manage their disease conditions with the aid of healthcare providers. Mobile health technology—also known as mHealth—supports healthcare practice by means of mobile devices such as smartphone applications, web-based technologies, telecommunications services, social media, and wearable technology, and is becoming increasingly popular. Many studies have evaluated the utility of mHealth as a tool to improve chronic disease management through monitoring and feedback, educational and lifestyle interventions, clinical decision support, medication adherence, risk screening, and rehabilitation support. The aim of this article is to summarize systematic reviews addressing the effect of mHealth on the outcome of patients with chronic diseases. We describe the current applications of various mHealth approaches, evaluate their effectiveness as well as limitations, and discuss potential challenges in their future development. The evidence to date indicates that none of the existing mHealth technologies are inferior to traditional care. Telehealth and web-based technologies are the most frequently reported interventions, with promising results ranging from alleviation of disease-related symptoms, improvement in medication adherence, and decreased rates of rehospitalization and mortality. The new generation of mHealth devices based on various technologies are likely to provide more efficient and personalized healthcare programs for patients.

Image segmentation for 3D printing and 3D visualization has become an essential component in many fields of medical research, teaching, and clinical practice. Medical image segmentation requires sophisticated computerized quantifications and visualization tools. Recently, with the development of artificial intelligence (AI) technology, tumors or organs can be quickly and accurately detected and automatically contoured from medical images. This paper introduces a platform-independent, multi-modality image registration, segmentation, and 3D visualization program, named artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization (AIMIS3D). YOLOV3 algorithm was used to recognize prostate organ from T2-weighted MRI images with proper training. Prostate cancer and bladder cancer were segmented based on U-net from MRI images. CT images of osteosarcoma were loaded into the platform for the segmentation of lumbar spine, osteosarcoma, vessels, and local nerves for 3D printing. Breast displacement during each radiation therapy was quantitatively evaluated by automatically identifying the position of the 3D printed plastic breast bra. Brain vessel from multi-modality MRI images was segmented by using model-based transfer learning for 3D printing and naked eye 3D visualization in AIMIS3D platform.

Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media.

Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users were included in the study population. Multinomial logistic regression and counterfactual analysis were conducted.

Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines, either pro-vaccine ($B=0.40,\mathrm{SE}=0.08,P<0.001,\mathrm{OR}=1.49;95\%\mathrm{CI}=1.26\text{--}1.75$) or anti-vaccine ($B=0.52,\mathrm{SE}=0.06,P<0.001,\mathrm{OR}=1.69;95\%\mathrm{CI}=1.49\text{--}1.91$). People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion ($B=-0.18,\mathrm{SE}=0.04,P<0.001,\mathrm{OR}=0.84;95\%\mathrm{CI}=0.77\text{--}0.90$). The United States public is most concerned about the safety, effectiveness, and political issues regarding vaccines for COVID-19, and improving personal pandemic experience increases the vaccine acceptance level.

Conclusion Opinion on COVID-19 vaccine uptake varies across people of different characteristics.

Background

Brain network describing interconnections between brain regions contains abundant topological information. It is a challenge for the existing statistical methods (e.g., t test) to investigate the topological differences of brain networks.

Methods

We proposed a kernel based statistic framework for identifying topological differences in brain networks. In our framework, the topological similarities between paired brain networks were measured by graph kernels. Then, graph kernels are embedded into maximum mean discrepancy for calculating kernel based test statistic. Based on this test statistic, we adopted conditional Monte Carlo simulation to compute the statistical significance (i.e., P value) and statistical power. We recruited 33 patients with Alzheimer's disease (AD), 33 patients with early mild cognitive impairment (EMCI), 33 patients with late mild cognitive impairment (LMCI) and 33 normal controls (NC) in our experiment. There are no statistical differences in demographic information between patients and NC. The compared state-of-the-art statistical methods include t test, t squared test, two-sample permutation test and non-normal test.

Results

We applied the proposed shortest path matched kernel to our framework for investigating the statistical differences of shortest path topological structures in brain networks of AD and NC. We compared our method with the existing state-of-the-art statistical methods in brain network characteristic including clustering coefficient and functional connection among EMCI, LMCI, AD, and NC. The results indicate that our framework can capture the statistically discriminative shortest path topological structures, such as shortest path from right rolandic operculum to right supplementary motor area (P = 0.00314, statistical power = 0.803). In clustering coefficient and functional connection, our framework outperforms the state-of-the-art statistical methods, such as P = 0.0013 and statistical power = 0.83 in the analysis of AD and NC.

Conclusion

Our proposed kernel based statistic framework not only can be used to investigate the topological differences of brain network, but also can be used to investigate the static characteristics (e.g., clustering coefficient and functional connection) of brain network.

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image analysis. The lungs, bone, and mediastinum were included in terms of anatomy, while X-ray and computed tomography (CT), with and without contrast enhancement, were considered regarding imaging modalities. Four key components of deep learning were summarized, namely, network architectures, learning strategies, optimization methods, and vision tasks. Disease-specific applications were discussed in detail with respect to the dimension of the data input, network architecture, and modality: lung cancer, pneumonia, tuberculosis, pulmonary embolism, chronic obstructive pulmonary disease, and interstitial lung disease for lung; traumatic fractures, osteoporosis, osteoporotic fractures, and bone metastases for bone; and coronary artery calcification and aortic dissection for vascular diseases. Finally, five promising research directions and possible solutions were presented for future work.