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| 1 | Thinking on the informatization development of China’s healthcare system in the post-COVID-19 era显示文摘With the application of Internet of Things,big data,cloud computing,artificial intelligence,and other cuttingedge technologies,China’s medical informatization is developing rapidly.In this paper,we summaried the role of information technology in healthcare sector’s battle against the coronavirus disease 2019(COVID-19)from the perspectives of early warning and monitoring,screening and diagnosis,medical treatment and scientific research,analyzes the bottlenecks of the development of information technology in the post-COVID-19 era,and puts forward feasible suggestions for further promoting the construction of medical informatization from the perspectives of sharing,convenience,and safety. | Ming Zhang Danyun Dai Siliang Hou Wei Liu Feng Gao Dong Xu Yu Hu | 2021 | Intelligent Medicine2021，1，1： | 4 |
| 2 | Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology显示文摘Background:The ongoing coronavirus disease 2019(COVID-19)pandemic has put radiologists at a higher risk of infection during the computer tomography(CT)examination for the patients.To help settling these problems,we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus(2019-nCoV)infection and to increase the examination efficiency,patient scanning accuracy and better image quality in chest CT imaging.Methods:From February 10 to April 12,2020,adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow(CW group)or an automatic contactless imaging workflow(AW group)in Wuhan Leishenshan Hospital(China)were retrospectively and prospectively enrolled in this study.The total examination time in two groups was recorded and compared.The patient compliance of breath holding,positioning accuracy,image noise and signal-to-noise ratio(SNR)were assessed by three experienced radiologists and compared between the two groups.Results:Compared with the CW group,the total positioning time of the AW group was reduced((118.0±20.0)s vs.(129.0±29.0)s,P=0.001),the proportion of scanning accuracy was higher(98%vs.93%),and the lung length had a significant difference((0.90±1.24)cm vs.(1.16±1.49)cm,P=0.009).For the lesions located in the pulmonary centrilobular and subpleural regions,the image noise in the AW group was significantly lower than that in the CW group(centrilobular region:(140.4±78.6)HU vs.(153.8±72.7)HU,P=0.028;subpleural region:(140.6±80.8)HU vs.(159.4±82.7)HU,P=0.010).For the lesions located in the peripheral,centrilobular and subpleural regions,SNR was significantly higher in the AW group than in the CW group(centrilobular region:6.6±4.3 vs.4.9±3.7,P=0.006;subpleural region:6.4±4.4 vs.4.8±4.0,P<0.001).Conclusions:The automatic contactless imaging workflow using intelligent guided robot and automatic posi-tioning technology allows for reducing the examination time and improving the patient’s compliance of breath holding,positioning accuracy and image quality in chest CT imaging. | Yadong Gang Xiongfeng Chen Hanlun Wang Jianying Li Ying Guo Bin Wen Jinxiang Hu Haibo Xu Xinghuan Wang | 2021 | Intelligent Medicine2021，1，1： | 2 |
| 3 | Ketamine use disorder:preclinical,clinical,and neuroimaging evidence to support proposed mechanisms of actions显示文摘Ketamine,a noncompetitive N-methyl-D-aspartate(NMDA)receptor antagonist,has been exclusively used as an anesthetic in medicine and has led to new insights into the pathophysiology of neuropsychiatric disorders.Clinical studies have shown that low subanesthetic doses of ketamine produce antidepressant effects for individuals with depression.However,its use as a treatment for psychiatric disorders has been limited due to its reinforcing effects and high potential for diversion and misuse.Preclinical studies have focused on understanding the molecular mechanisms underlying ketamine’s antidepressant effects,but a precise mechanism had yet to be elucidated.Here we review different hypotheses for ketamine’s mechanism of action including the direct inhibition and disinhibition of NMDA receptors,aminomethylphosphonic acid receptors(AMPAR)activation,and heightened activation of monoaminergic systems.The proposed mechanisms are not mutually exclusive,and their combined influence may exert the observed structural and functional neural impairments.Long term use of ketamine induces brain structural,functional impairments,and neurodevelopmental effects in both rodents and humans.Its misuse has increased rapidly in the past 20 years and is one of the most common addictive drugs used in Asia.The proposed mechanisms of action and supporting neuroimaging data allow for the development of tools to identify‘biotypes’of ketamine use disorder(KUD)using machine learning approaches,which could inform intervention and treatment. | Leah Vines Diana Sotelo Allison Johnson Evan Dennis Peter Manza Nora D.Volkow Gene-Jack Wang | 2022 | Intelligent Medicine2022，2，2： | 2 |
| 4 | Computational intelligence in solving bioinformatics problems显示文摘 | Cios K J Mamitsuka H Nagashima T | 2005 | Artificial Intelligence in Medicine2005，35，12： | 1 |
| 5 | A reliable method for cell phenotype image classification 显示文摘 | Nanni L Lumini A | 2008 | Artificial Intelligence in Medicine2008，43，2： | 1 |
| 6 | Use of Genetic Algorithms for Neural Networks to Predict Community-acquired Pneumonia显示文摘 | Paul S H Ben S G Thomas G T | 2004 | Artificial Intelligence in Medicine2004，30，： | 1 |
| 7 | Analysis of adverse drug reactions using drug and drug target interactions and graph-based metheds显示文摘 | Lin SF Xiao KT Huang YT | 2010 | Artificial Intelligence in Medicine2010，48，23： | 1 |
| 8 | Learning differential diagnosis of erythemato squamous diseases using voting feature intervals显示文摘 | Güvenir H Demiroz G Ilter N | 1998 | Artificial Intelligence in Medicine1998，13，3： | 1 |
| 9 | Smart wearable systems: Current status and future challenges显示文摘 | Chan M Est~ve D Fourniols J Y | 2012 | Artificial intelligence in medicine2012，56，3： | 1 |
| 10 | Extracting multi-source brain activity from a single electromagnetic channel显示文摘 | Christopher J J David L | 2003 | Artificial Intelligence in Medicine2003，28，1： | 1 |
| 11 | Self-organizing map for cluster analysis of a breast cancer database显示文摘 | Mia K M Joseph Y L Georgia D T | 2003 | Artificial Intelligence in Medicine2003，27，： | 1 |
| 12 | Multiple kernel learning in protein-protein interaction extraction from biomedical literature显示文摘 | Yang Zhihao Tang Nan Zhang Xiao | 2011 | Artificial Intelligence in Medicine2011，51，3： | 1 |
| 13 | From Certainty Factors to Belief Net-works显示文摘 | Heckerman D E Shortliffe E H | 1992 | Artificial Intelligence in Medicine1992，4，1： | 1 |
| 14 | A novel method for automated EMG decomposition and MUAP classification 显示文摘 | Katsis CD Goletsis Y Likas A | 2006 | Artificial Intelligence in Medicine2006，37，1： | 1 |
| 15 | A Markov decision process approach to multi-category patient scheduling in a diagnostic facility显示文摘 | Gocguna Y et ai | 2011 | Artificial Intelligence in Medicine2011，381，： | 1 |
| 16 | Extracting multisource brain activity from a single electromagnetic channel显示文摘 | James C J Lowe D | 2003 | Artificial Intelligence in Medicine2003，28，6： | 1 |
| 17 | Mining of ReLations Between Proteins over Biomedical Scientific Literature Using a Deep -linguistic Approach显示文摘 | Rinaldi F Schneider G Kaljurand K | 2007 | Artificial Intelligence in Medicine2007，39，2： | 1 |
| 18 | Flexible guideline-based patient care flow systems 显示文摘 | Silvana Quaglini | 2001 | Artificial Intelligence in Medicine2001，22，： | 1 |
| 19 | Digital lung - mechanics simulator as a valida- tion tool 显示文摘 | Gross H Fohring U | 1993 | Artificial Intelligence in Medicine1993，10，2： | 1 |
| 20 | Multi-scaled morphological features for the characterization of mammographie masses using statistical classification schemes显示文摘 | Harris Georgiou Michael Mavroforakis Nikos Dimitropoulos | 2007 | Journal of Artificial Intelligence in Medicine2007，41，8： | 1 |
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