Latest Issue
Volume 34 ， Issue 2 ， 2019
- Abstract：This special issue focuses on recent advances of medical AIs. It includes review articles for the key AI algorithms and technologies used in medicine, the current status of computer-aided diagnosis in several fields or diseases which are heavily studied, the examples of applications of AI in various forms, and the potential way that AI can cooperate and change the common practice of health system. We hope this issue can give experts and practitioners in medicine a comprehensive view of the status quo of medical AI and inspire more ideas for further development of this realm.41|547|0Updated：2024-04-10
Guest Editor’s Letter
- Abstract：Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future. In this article, we review the recent progress of AI-enabled medical imaging. Firstly, we briefly review the background about AI in its way of evolution. Then, we discuss the recent successes of AI in different medical imaging tasks, especially in image segmentation, registration, detection and recognition. Also, we illustrate several representative applications of AI-enabled medical imaging to show its advantage in real scenario, which includes lung nodule in chest CT, neuroimaging, mammography, and etc. Finally, we report the way of human-machine interaction. We believe that, in the future, AI will not only change the traditional way of medical imaging, but also improve the clinical routines of medical care and enable many aspects of the medical society.Keywords：medical imaging;artificial intelligence;deep learning;image segmentation;image registration;image detection;image recognition93|1402|3Updated：2024-04-10
Editorial
- Abstract：Artificial intelligence (AI) is rapidly being applied to a wide range of fields, including medicine, and has been considered as an approach that may augment or substitute human professionals in primary healthcare. However, AI also raises several challenges and ethical concerns. In this article, the author investigates and discusses three aspects of AI in medicine and healthcare: the application and promises of AI, special ethical concerns pertaining to AI in some frontier fields, and suggestive ethical governance systems. Despite great potentials of frontier AI research and development in the field of medical care, the ethical challenges induced by its applications has put forward new requirements for governance. To ensure “trustworthy” AI applications in healthcare and medicine, the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested. The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system.Keywords：artificial intelligence;medical ethics;ethical governance;machine learning;brain-computer interaction;brain-inspired computer;robots;biohybrids356|5879|1Updated：2024-04-10
- Abstract：In recent years, artificial intelligence (AI) has developed rapidly in the field of medical imaging. However, the collaborations among hospitals, research institutes and enterprises are insufficient at the present, and there are various issues in technological transformation and value landing of products in this area. To solve the core problems in the developmental path of medical imaging AI, the Chinese Innovative Alliance of Industry, Education, Research and Application of Artificial Intelligence for Medical Imaging compiled the White Paper on Medical Image AI in China. This article introduces the current status of collaboration, the clinical demands for medical imaging AI technique, and the key points in AI technology transformation: robustness, usability and security. We are facing challenges of lacking industry standards, data desensitization standard, assessment system, as well as corresponding regulations and policies to realize the application values of AI products in medical imaging. Further development of AI in medical imaging requires breakthroughs of the core algorithm, deep involvement of doctors, input from capitals, patience from societies, and most importantly, the resolutions from government for multiple difficulties in links of landing the technology.Keywords：medical imaging;artificial intelligence;white paper;innovative alliance57|1186|1Updated：2024-04-10
Perspectives
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Keywords：white paper;medical imaging;artificial intelligence89|1290|0Updated：2024-04-10
Journal Watch
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Abstract：Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management. It is a common requirement to reuse the data for clinical research. However, we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform. In this paper, we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology. We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform. Secondly, we built special disease case repositories (i.e., heart failure repository) that utilize the graph to search the related patients and to normalize the data. Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure, we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository. After the propensity score matching, the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired. Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients. This paper presents the workflow and application example of big data mining based on regional EHR data.Keywords：electronic health records;clinical terminology knowledge graph;clinical special disease case repository;evaluation of data quality;large scale cohort study43|1178|0Updated：2024-04-10
Special Report
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Abstract：With the continuous progress of virtual simulation technology, medical surgery visualization system has been developed from two-dimensional to three-dimensional, from digital to network and intelligentization. The visualization system with mixed reality technology will also be used in all stage of medical surgery, such as case discussion, surgical planning, intraoperative guidance, post-operative evaluation, rehabilitation, so as to further promote high intelligence, high precision of medical surgery, and consequently improve effectiveness of treatment and quality of medical service. This paper discusses the composition and technical characteristics of medical operation visualization system based on mixed reality technology, and introduces some typical applications of mixed reality technology in medical operation visualization, which provides a new perspective for the application of mixed technology in medical surgery.Keywords：mixed reality;medical surgery;visualization;simulation56|1373|4Updated：2024-04-10 -
Abstract：Brain region-of-interesting (ROI) segmentation is an important prerequisite step for many computer-aid brain disease analyses. However, the human brain has the complicated anatomical structure. Meanwhile, the brain MR images often suffer from the low intensity contrast around the boundary of ROIs, large inter-subject variance and large inner-subject variance. To address these issues, many multi-atlas based segmentation methods are proposed for brain ROI segmentation in the last decade. In this paper, multi-atlas based methods for brain MR image segmentation were reviewed regarding several registration toolboxes which are widely used in the multi-atlas methods, conventional methods for label fusion, datasets that have been used for evaluating the multi-atlas methods, as well as the applications of multi-atlas based segmentation in clinical researches. We propose that incorporating the anatomical prior into the end-to-end deep learning architectures for brain ROI segmentation is an important direction in the future.Keywords：multi-atlas;brain;segmentation;magnetic resonance43|1232|0Updated：2024-04-10 -
Abstract：Diabetic retinopathy (DR) is one of the leading causes of vision loss and can be effectively avoided by screening, early diagnosis and treatment. In order to increase the universality and efficiency of DR screening, many efforts have been invested in developing intelligent screening, and there have been great advances. In this paper, we survey DR screening from four perspectives: 1) public color fundus image datasets of DR; 2) DR classification and related lesion-extraction approaches; 3) existing computer-aided systems for DR screening; and 4) existing issues, challenges, and research trends. Our goal is to provide insights for future research directions on DR intelligent screening.Keywords：diabetes;retinopathy;fundus images;intelligent screening46|1192|0Updated：2024-04-10
Review
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Abstract：Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR), which are the important digital carriers for recording medical activities of patients. Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE. This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks. In the data preprocessing of both tasks, a GloVe word embedding model was used to vectorize words. In the NER task, a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer. In the MRE task, the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset, the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks, where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task. Moreover, the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction. It also verified the feasibility of the BiLSTM-CRF model in different application scenarios, laying the foundation for the subsequent work in the EMR field.Keywords：medical knowledge extraction;electronic medical record;named entity recognition;medical relation extraction;deep learning;bidirectional long short-term memory;conditional random field76|2568|3Updated：2024-04-10 - Abstract：Objectives This study aim to evaluate patient’s perception about anesthesiologists’ job roles and investigate their expectations for anesthesia care.Methods We designed a self-administered questionnaire for this cross-sectional survey study and delivered questionnaire forms to adult in-patients who were scheduled for elective surgery before pre-operative anesthetic visit the day before surgery. We collected information of respondents’ demographic data, education background, health literacy and previous experience of anesthesia, perception of anesthesiologist’s job, the expectation on anesthesia care. Descriptive analyses, χ
[2] test and multiple linear regression analysis were used for data analysis. Results Of 550 participants, 521（94.7%）completed the questionnaire. In these respondents, 335 (64.3%) considered anesthesiology as an independent medical discipline, 225 (43.2%) believed that anesthesiology department was an independent clinical department, and 243 (46.6%) recognized anesthesiologists as qualified doctors. Only 21.5% of them knew that anesthesiologists also work in the intensive care unit and 26.9% of them knew that anesthesiologists also work in pain clinic as well. Younger patients (β=-0.044, P<0.001), those with higher education (β=1.200, P<0.001), or with better health literacy (β=0.781, P=0.005) had significant more knowledge about the job roles of anesthesiologists. Most patients demanded pre-anesthetic visit (80.5%), expected availability of preoperative anesthetic clinic (74.1%), wished to receive more information about anesthesia (91.3%) and anesthesiologist (77.4%).Conclusions Patients’ perception about anesthesiologists might be limited. Efforts should be made on education about anesthesia, especially for elderly patients and those under-educated patients. Preoperative anesthetic clinic is expected by most in-patients.Keywords：patients’ perception;;anesthesiology;anesthesiologist;preoperative clinic22|911|1Updated：2024-04-10 -
Abstract：Objective This study aimed to verify the association between osteoprotegerin gene (OPG) and its variants with osteoporosis (OP) by performing integrative analysis.Methods We used the KGG software to perform gene-based association analysis, which integrated all publicly available single-nucleotide polymorphism (SNP)-based P values and obtained an overall P value for the OPG. The significant SNPs were screened for expression quantitative trait loci (eQTLs). Meta-analysis was used to combine the associations between the variants of OPG and bone mineral density (BMD) reported in the literatures. Then we performed dual-luciferase reporter gene systems for the functional verification of the variants of OPG in vitro.Results In the gene-based association analysis, the over all P value of OPG was 6.24×10 -13for BMD at femoral neck (FN) and 7.37×10 -17 for BMD at lumbar spine (LS), indicating the importance of OPG for OP. The publicly available eQTL database identified 5 eQTLs which exert cis-regulation effects on OPG at FN and LS. Literature searching found that rs2073617 (known as T950C) was the hot spot SNP. There were 13 relevant studies on rs2073617 besides the GEFOS-2 study identified from the PubMed. Significant differences among TT, TC and CC genotypes at FN (P= 0.047) and LS (P= 0.025) were shown by meta-analysis, demonstrating the associations between T950C polymorphism and BMD. Luciferase gene expression was significantly higher at the presence of allele C than allele T in the 293T cells (t=-9.47, P<0.01). Conclusion The integrative analysis further confirmed the importance of OPG in OP and the correlation of T950C polymorphism with BMD of OP. The strategy can be used as a reference for functional interpretation of other disease-related genes.Keywords：osteoprotegerin;osteoporosis;single-nucleotide polymorphism;integrative analysis22|861|0Updated：2024-04-10
Original Article
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