Intelligent medicine最新文献

{"title":"Guide for Authors","authors":"","doi":"10.1016/S2667-1026(21)00137-6","DOIUrl":"https://doi.org/10.1016/S2667-1026(21)00137-6","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 1","pages":"Pages 54-60"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102621001376/pdfft?md5=2a0a5fff15836015c16046f840aa8bd1&pid=1-s2.0-S2667102621001376-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136816430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based medical image segmentation for 3D printing and naked eye 3D visualization","authors":"Guang Jia ,&nbsp;Xunan Huang ,&nbsp;Sen Tao ,&nbsp;Xianghuai Zhang ,&nbsp;Yue Zhao ,&nbsp;Hongcai Wang ,&nbsp;Jie He ,&nbsp;Jiaxue Hao ,&nbsp;Bo Liu ,&nbsp;Jiejing Zhou ,&nbsp;Tanping Li ,&nbsp;Xiaoling Zhang ,&nbsp;Jinglong Gao","doi":"10.1016/j.imed.2021.04.001","DOIUrl":"10.1016/j.imed.2021.04.001","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 1","pages":"Pages 48-53"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.04.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"112350185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination","authors":"Hanjia Lyu ,&nbsp;Junda Wang ,&nbsp;Wei Wu ,&nbsp;Viet Duong ,&nbsp;Xiyang Zhang ,&nbsp;Timothy D. Dye ,&nbsp;Jiebo Luo","doi":"10.1016/j.imed.2021.08.001","DOIUrl":"10.1016/j.imed.2021.08.001","url":null,"abstract":"<div><p><strong>Background</strong> 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.</p><p><strong>Methods</strong> 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.</p><p><strong>Results</strong> Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines, either pro-vaccine (<span><math><mrow><mi>B</mi><mo>=</mo><mn>0.40</mn><mo>,</mo><mspace></mspace><mi>SE</mi><mo>=</mo><mn>0.08</mn><mo>,</mo><mi>P</mi><mo>&lt;</mo><mn>0.001</mn><mo>,</mo><mi>OR</mi><mo>=</mo><mn>1.49</mn><mo>;</mo><mn>95</mn><mo>%</mo><mi>CI</mi><mo>=</mo><mn>1.26</mn><mtext>--</mtext><mn>1.75</mn></mrow></math></span>) or anti-vaccine (<span><math><mrow><mi>B</mi><mo>=</mo><mn>0.52</mn><mo>,</mo><mspace></mspace><mi>SE</mi><mo>=</mo><mn>0.06</mn><mo>,</mo><mspace></mspace><mspace></mspace><mi>P</mi><mo>&lt;</mo><mn>0.001</mn><mo>,</mo><mspace></mspace><mi>OR</mi><mo>=</mo><mn>1.69</mn><mo>;</mo><mspace></mspace><mn>95</mn><mo>%</mo><mspace></mspace><mi>CI</mi><mo>=</mo><mn>1.49</mn><mtext>--</mtext><mn>1.91</mn></mrow></math></span>). People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion (<span><math><mrow><mi>B</mi><mo>=</mo><mo>−</mo><mn>0.18</mn><mo>,</mo><mspace></mspace><mi>SE</mi><mo>=</mo><mn>0.04</mn><mo>,</mo><mspace></mspace><mi>P</mi><mo>&lt;</mo><mn>0.001</mn><mo>,</mo><mspace></mspace><mi>OR</mi><mo>=</mo><mn>0.84</mn><mo>;</mo><mspace></mspace><mn>95</mn><mo>%</mo><mspace></mspace><mi>CI</mi><mo>=</mo><mn>0.77</mn><mtext>--</mtext><mn>0.90</mn></mrow></math></span>). 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.</p><p><strong>Conclusion</strong> Opinion on COVID-19 vaccine uptake varies across people of different characteristics.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 1","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.08.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39365209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kernel based statistic: identifying topological differences in brain networks","authors":"Kai Ma,&nbsp;Wei Shao,&nbsp;Qi Zhu,&nbsp;Daoqiang Zhang","doi":"10.1016/j.imed.2021.06.002","DOIUrl":"10.1016/j.imed.2021.06.002","url":null,"abstract":"<div><h3><strong><em>Background</em></strong></h3><p>Brain network describing interconnections between brain regions contains abundant topological information. It is a challenge for the existing statistical methods (e.g., <em>t</em> test) to investigate the topological differences of brain networks.</p></div><div><h3><strong><em>Methods</em></strong></h3><p>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., <em>P</em> 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 <em>t</em> test, <em>t</em> squared test, two-sample permutation test and non-normal test.</p></div><div><h3><strong><em>Results</em></strong></h3><p>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 (<em>P</em> = 0.00314, <em>statistical power</em> = 0.803). In clustering coefficient and functional connection, our framework outperforms the state-of-the-art statistical methods, such as <em>P</em> = 0.0013 and <em>statistical power</em> = 0.83 in the analysis of AD and NC.</p></div><div><h3><strong><em>Conclusion</em></strong></h3><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"2 1","pages":"Pages 30-40"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.06.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44048464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The application of artificial intelligence to chest medical image analysis","authors":"Feng Liu ,&nbsp;Jie Tang ,&nbsp;Jiechao Ma ,&nbsp;Cheng Wang ,&nbsp;Qing Ha ,&nbsp;Yizhou Yu ,&nbsp;Zhen Zhou","doi":"10.1016/j.imed.2021.06.004","DOIUrl":"10.1016/j.imed.2021.06.004","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 3","pages":"Pages 104-117"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.06.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46709731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in clinical genetics and genomics","authors":"Sen Zhao ,&nbsp;Xi Cheng ,&nbsp;Wen Wen ,&nbsp;Guixing Qiu ,&nbsp;Terry Jianguo Zhang ,&nbsp;Zhihong Wu ,&nbsp;Nan Wu","doi":"10.1016/j.imed.2021.03.005","DOIUrl":"10.1016/j.imed.2021.03.005","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 3","pages":"Pages 128-133"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.03.005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47777871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the pathological status of mammographic microcalcifications through a radiomics approach","authors":"Min Li ,&nbsp;Liyu Zhu ,&nbsp;Guangquan Zhou ,&nbsp;Jianan He ,&nbsp;Yanni Jiang ,&nbsp;Yang Chen","doi":"10.1016/j.imed.2021.05.003","DOIUrl":"10.1016/j.imed.2021.05.003","url":null,"abstract":"<div><p><strong>Objective</strong> 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).</p><p><strong>Methods</strong> We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4 (training cohort, <em>n</em> = 428; independent testing cohort, <em>n</em> = 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.</p><p><strong>Results</strong> 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.</p><p><strong>Conclusion</strong> 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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 3","pages":"Pages 95-103"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.05.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"106212284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guide for Authors","authors":"","doi":"10.1016/S2667-1026(21)00103-0","DOIUrl":"https://doi.org/10.1016/S2667-1026(21)00103-0","url":null,"abstract":"","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 3","pages":"Pages 137-143"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102621001030/pdfft?md5=2bd39635b60b8ddcf224ed11178fff73&pid=1-s2.0-S2667102621001030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136837532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey on deep learning in medical image reconstruction","authors":"Emmanuel Ahishakiye ,&nbsp;Martin Bastiaan Van Gijzen ,&nbsp;Julius Tumwiine ,&nbsp;Ruth Wario ,&nbsp;Johnes Obungoloch","doi":"10.1016/j.imed.2021.03.003","DOIUrl":"10.1016/j.imed.2021.03.003","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 3","pages":"Pages 118-127"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.imed.2021.03.003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"99701080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital inclusion of older people: harnessing digital technologies to promote healthy ageing in the Western Pacific Region","authors":"Shan Xu,&nbsp;Dong Min,&nbsp;Yiwen Cheng,&nbsp;Peng Wang,&nbsp;Yue Gao","doi":"10.1016/j.imed.2021.08.002","DOIUrl":"10.1016/j.imed.2021.08.002","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"1 3","pages":"Pages 134-136"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667102621000401/pdfft?md5=47dbfbc71e04cbd4a0d9b9e9df572295&pid=1-s2.0-S2667102621000401-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43823536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}