Intelligent medicine最新文献

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.

Developments in genetics and genomics are progressing at an unprecedented speed. Twenty years ago, the human genome project provided the first glimpses into the human genome sequence and launched a new era of human genetics. The emerging of next-generation sequencing (NGS) in 2005 then made possible comprehensive genetic testing such as exome sequencing and genome sequencing. Meanwhile, great efforts have been put into the optimization of bioinformatic pipelines to make increasingly speedy and accurate variant analyses based on NGS data. These advances in sequencing technologies and analytical methods have revolutionized the diagnostic odyssey of suspected hereditary diseases. More recently, the genotype-phenotype relationship and polygenic risk scores (PRSs) generated from genome-wide association studies have expanded our horizon from rare genetic mutations to a genomic landscape implicated by the combined effect of both rare variants and polymorphisms. At the same time, clinicians and genetic counselors are facing huge challenges conferred by overwhelming genomic knowledge and long sheets of testing reports for comprehensive genomic sequencing. The path toward the “next-generation” clinical genetics and genomics may underlie semiautomatic pipelines assisted by artificial intelligence techniques.

Objective The study aimed to develop a machine learning (ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications (MCs).

Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4 (training cohort, n = 428; independent testing cohort, n = 35) in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019. Subsequently, 837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoost-embedded recursive feature elimination technique (RFE), followed by four machine learning-based classifiers to build the radiomics signature.

Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression (LR) and support vector machine (SVM) yielded better positive predictive value (PPV)/sensitivity (SE), 0.904 (95% CI, 0.865–0.949)/0.946 (95% CI, 0.929–0.977) and 0.891 (95% CI, 0.822–0.939)/0.939 (95% CI, 0.907–0.973) respectively, outperforming their negative predictive value (NPV)/specificity (SP) from 10-fold cross-validation (10FCV) of the training cohort. The optimal prognostic model was obtained by SVM with an area under the curve (AUC) of 0.906 (95% CI, 0.834–0.969) and accuracy (ACC) 0.787 (95% CI, 0.680–0.855) from 10FCV against AUC 0.810 (95% CI, 0.760–0.960) and ACC 0.800 from the testing cohort.

Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

Medical image reconstruction aims to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Deep learning and its applications in medical imaging, especially in image reconstruction have received considerable attention in the literature in recent years. This study reviews records obtained electronically through the leading scientific databases (Magnetic Resonance Imaging journal, Google Scholar, Scopus, Science Direct, Elsevier, and from other journal publications) searched using three sets of keywords: (1) Deep learning, image reconstruction, medical imaging; (2) Medical imaging, Deep learning, Image reconstruction; (3) Open science, Open imaging data, Open software. The articles reviewed revealed that deep learning-based reconstruction methods improve the quality of reconstructed images qualitatively and quantitatively. However, deep learning techniques are generally computationally expensive, require large amounts of training datasets, lack decent theory to explain why the algorithms work, and have issues of generalization and robustness. The challenge of lack of enough training datasets is currently being addressed by using transfer learning techniques.

Adapting systems and technology for an aging population has benefits for older people, the consumer market industry itself and all of society. To promote knowledge sharing on innovations for healthy ageing and digital inclusion of older people in the Western Pacific Region, a hybrid conference on “Digital inclusion of older people: harnessing digital technologies to promote healthy ageing in the Western Pacific Region” was held on 23 June 2021 by China Academy of Information and Communications Technology, a WHO Collaborating Centre for Digital Health. Barriers from demand side include: (1) unaffordability; (2) poor Information and Communication technology (ICT) knowledge and skills for navigation; and (3) low self-efficacy and motivation. Supply barriers include: (1) youth-centred design; (2) ageism; and (3) anti-facilitative environment including infrastructure and age-biased technology. Existing practices to overcome digital inclusion barriers were shared: (1) landmark initiatives related to the health and social welfare; (2) laws and policies to improve aged care services, strengthen social services, enrich spiritual and cultural life for older people; (3) ICT infrastructure and residential care facilities based on the philosophy of family care and supported by community care; (4) affordable digital application and adaptive feature design to better enable and motivate their desire to use digital technology; and (5) community activities such as trainings and tutorials to enhance digital capacity and literacy of older people. Main principles highlighted include market motivation, human-centered design, creating enabling environments, and multi-stakeholder collaborations to provide guidance to customize strategy under context of different regions and countries, instead of a one-size-fits-all solution, to avoid the risk of exacerbating inequalities experienced by older people, caused by accelerated ICT innovation, and advocate for more affordable products in the silver market.

Medical artificial intelligence (AI) is an important technical asset to support medical supply-side reforms and national development in the big data era. Clinical data from multiple disciplines represent building blocks for the development and application of AI-aided diagnostic and treatment systems based on medical big data. However, the inconsistent quality of these data resources in AI research leads to waste and inefficiencies. Therefore, it is crucial that the field formulates the requirements and content related to data processing as part of the development of intelligent medicine. To promote medical AI research worldwide, the “Belt and Road” International Ophthalmic Artificial Intelligence Research and Development Alliance will establish a series of expert recommendations for data quality in intelligent medicine.

In recent years, the incidence of myopia has increased at an alarming rate among children and adolescents in China. The exploration of an effective prevention and control method for myopia is in urgent need. With the development of information technology in the past decade, artificial intelligence with the Internet of Things technology (AIoT) is characterized by strong computing power, advanced algorithm, continuous monitoring, and accurate prediction of long-term progression. Therefore, big data and artificial intelligence technology have the potential to be applied to data mining of myopia etiology and prediction of myopia occurrence and development. More recently, there has been a growing recognition that myopia study involving AIoT needs to undergo a rigorous evaluation to demonstrate robust results.