BJR open最新文献

{"title":"Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe.","authors":"Gemma Walsh, Nikolaos Stogiannos, Riaan van de Venter, Clare Rainey, Winnie Tam, Sonyia McFadden, Jonathan P McNulty, Nejc Mekis, Sarah Lewis, Tracy O'Regan, Amrita Kumar, Merel Huisman, Sotirios Bisdas, Elmar Kotter, Daniel Pinto Dos Santos, Cláudia Sá Dos Reis, Peter van Ooijen, Adrian P Brady, Christina Malamateniou","doi":"10.1259/bjro.20230033","DOIUrl":"10.1259/bjro.20230033","url":null,"abstract":"<p><p>Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20230033"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47931132","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":"Radiology and the medical student: do increased hours of teaching translate to more radiologists?","authors":"Aisha Shaheen Hameed, Aneesa K Hameed","doi":"10.1259/bjro.20230029","DOIUrl":"10.1259/bjro.20230029","url":null,"abstract":"","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20230029"},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46536598","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 in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018-2019.","authors":"Patricia Logullo, Angela MacCarthy, Paula Dhiman, Shona Kirtley, Jie Ma, Garrett Bullock, Gary S Collins","doi":"10.1259/bjro.20220033","DOIUrl":"10.1259/bjro.20220033","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant.</p><p><strong>Methods: </strong>In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively.</p><p><strong>Results: </strong>The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches.</p><p><strong>Conclusion: </strong>The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications.</p><p><strong>Advances in knowledge: </strong>We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models' outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"5 1","pages":"20220033"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9730154","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 in cardiovascular imaging: enhancing image analysis and risk stratification.","authors":"Andrew Lin, Konrad Pieszko, Caroline Park, Katarzyna Ignor, Michelle C Williams, Piotr Slomka, Damini Dey","doi":"10.1259/bjro.20220021","DOIUrl":"10.1259/bjro.20220021","url":null,"abstract":"<p><p>In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"5 1","pages":"20220021"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10104101","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":"CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls.","authors":"Daniel Liu, Neil C Binkley, Alberto Perez, John W Garrett, Ryan Zea, Ronald M Summers, Perry J Pickhardt","doi":"10.1259/bjro.20230014","DOIUrl":"10.1259/bjro.20230014","url":null,"abstract":"<p><strong>Objective: </strong>Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk.</p><p><strong>Methods: </strong>In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve.</p><p><strong>Results: </strong>Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657.</p><p><strong>Conclusion: </strong>Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity.</p><p><strong>Advances in knowledge: </strong>There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"1 1","pages":"20230014"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41891258","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 role of artificial intelligence in hastening time to recruitment in clinical trials.","authors":"Abdalah Ismail, Talha Al-Zoubi, Issam El Naqa, Hina Saeed","doi":"10.1259/bjro.20220023","DOIUrl":"10.1259/bjro.20220023","url":null,"abstract":"<p><p>Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20220023"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43154960","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":"Clinicoradiological outcomes after radical radiotherapy for lung cancer in patients with interstitial lung disease.","authors":"Gerard M Walls,&nbsp;Michael McMahon,&nbsp;Natasha Moore,&nbsp;Patrick Nicol,&nbsp;Gemma Bradley,&nbsp;Glenn Whitten,&nbsp;Linda Young,&nbsp;Jolyne M O'Hare,&nbsp;John Lindsay,&nbsp;Ryan Connolly,&nbsp;Dermot Linden,&nbsp;Peter A Ball,&nbsp;Gerard G Hanna,&nbsp;Jonathan McAleese","doi":"10.1259/bjro.20220049","DOIUrl":"10.1259/bjro.20220049","url":null,"abstract":"<p><strong>Objective: </strong>Interstitial lung disease (ILD) is relatively common in patients with lung cancer with an incidence of 7.5%. Historically pre-existing ILD was a contraindication to radical radiotherapy owing to increased radiation pneumonitis rates, worsened fibrosis and poorer survival compared with non-ILD cohorts. Herein, the clinical and radiological toxicity outcomes of a contemporaneous cohort are described.</p><p><strong>Methods: </strong>Patients with ILD treated with radical radiotherapy for lung cancer at a regional cancer centre were collected prospectively. Radiotherapy planning, tumour characteristics, and pre- and post-treatment functional and radiological parameters were recorded. Cross-sectional images were independently assessed by two Consultant Thoracic Radiologists.</p><p><strong>Results: </strong>Twenty-seven patients with co-existing ILD received radical radiotherapy from February 2009 to April 2019, with predominance of usual interstitial pneumonia subtype (52%). According to ILD-GAP scores, most patients were Stage I. After radiotherapy, localised (41%) or extensive (41%) progressive interstitial changes were noted for most patients yet dyspnoea scores (<i>n</i> = 15 available) and spirometry (<i>n</i> = 10 available) were stable. One-third of patients with ILD went on to receive long-term oxygen therapy, which was significantly more than the non-ILD cohort. Median survival trended towards being worse compared with non-ILD cases (17.8 <i>vs</i> 24.0 months, <i>p</i> = 0.834).</p><p><strong>Conclusion: </strong>Radiological progression of ILD and reduced survival were observed post-radiotherapy in this small cohort receiving lung cancer radiotherapy, although a matched functional decline was frequently absent. Although there is an excess of early deaths, long-term disease control is achievable.</p><p><strong>Advances in knowledge: </strong>For selected patients with ILD, long-term lung cancer control without severely impacting respiratory function may be possible with radical radiotherapy, albeit with a slightly higher risk of death.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"5 1","pages":"20220049"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9730153","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":"Spectrum of MRI findings of foetal alcohol syndrome disorders-what we know and what we need to know!","authors":"Saad Sharif, Naeha Lakshmanan, Farhana Sharif, Stephanie Ryan","doi":"10.1259/bjro.20210063","DOIUrl":"10.1259/bjro.20210063","url":null,"abstract":"<p><p>The exposure to alcohol <i>in utero</i> has been known to damage the developing foetus. Foetal alcohol spectrum disorders is an umbrella term that highlights a range of adverse effects linked to alcohol exposure <i>in utero</i>. Multiple studies have shown specific brain abnormalities, including a reduction in brain size, specifically in the deep nuclei and cerebellum, and parietal and temporal lobe white matter changes. While studies ascertained that other prenatal risk factors, such as maternal use of illicit drugs or lack of pre-natal care, and post-natal risk factors, such as physical or sexual abuse and low socioeconomic status, may be involved in the pathology of variances in foetal neurological abnormalities, prenatal alcohol exposure remained the strongest factor for effects on brain structure and function. Particularly, the number of days of alcohol consumption per week and drinking during all three trimesters of the pregnancy indicating the strongest relationship with brain abnormalities. Further studies are needed to explain pre-natal risk factors in isolation as well as in combination for neurodevelopmental outcomes. The diverse phenotypic presentations described indicate that the diagnostic criteria of foetal alcohol spectrum disorder must be refined to better represent the range of neurologic anomalies.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":" ","pages":"20210063"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44745116","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":"Assessing the implementation of COVID-19 structured reporting templates for chest radiography: a scoping review.","authors":"Peter A O'Reilly, Sarah Lewis, Warren Reed","doi":"10.1259/bjro.20220058","DOIUrl":"10.1259/bjro.20220058","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;One of the common modalities used in imaging COVID-19 positive patients is chest radiography (CXR), and serves as a valuable imaging method to diagnose and monitor a patients' condition. Structured reporting templates are regularly used for the assessment of COVID-19 CXRs and are supported by international radiological societies. This review has investigated the use of structured templates for reporting COVID-19 CXRs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A scoping review was conducted on literature published between 2020 and 2022 using Medline, Embase, Scopus, Web of Science, and manual searches. An essential criterion for the inclusion of the articles was the use of reporting methods employing either a structured quantitative or qualitative reporting method. Thematic analyses of both reporting designs were then undertaken to evaluate utility and implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Fifty articles were found with the quantitative reporting method used in 47 articles whilst 3 articles were found employing a qualitative design. Two quantitative reporting tools (Brixia and RALE) were used in 33 studies, with other studies using variations of these methods. Brixia and RALE both use a posteroanterior or supine CXR divided into sections, Brixia with six and RALE with four sections. Each section is scaled numerically depending on the level of infection. The qualitative templates relied on selecting the best descriptor of the presence of COVID-19 radiological appearances. Grey literature from 10 international professional radiology societies were also included in this review. The majority of the radiology societies recommend a qualitative template for reporting COVID-19 CXRs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Most studies employed quantitative reporting methods which contrasted with the structured qualitative reporting template advocated by most radiological societies. The reasons for this are not entirely clear. There is also a lack of research literature on both the implementation of the templates or comparing both template types, indicating that the use of structured radiology reporting types may be an underdeveloped clinical strategy and research methodology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Advances in knowledge: &lt;/strong&gt;This scoping review is unique in that it has undertaken an examination of the utility of the quantitative and qualitative structured reporting templates for COVID-19 CXRs. Moreover, through this review, the material examined has allowed a comparison of both instruments, clearly showing the favoured style of structured reporting by clinicians. At the time of the database interrogation, there were no studies found had undertaken such examinations of both reporting instruments. Moreover, due to the enduring influence of COVID-19 on global health, this scoping review is timely in examining the most innovative structured reporting tools that could be used in the reporting of COVID-19 CXRs. This report could assist cli","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"5 1","pages":"20220058"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10301714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9736567","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":"Imaging in patients with acute dyspnea when cardiac or pulmonary origin is suspected.","authors":"Ruxandra-Iulia Milos, Carmen Bartha, Sebastian Röhrich, Benedikt H Heidinger, Florian Prayer, Lucian Beer, Christian Wassipaul, Daria Kifjak, Martin L Watzenboeck, Svitlana Pochepnia, Helmut Prosch","doi":"10.1259/bjro.20220026","DOIUrl":"10.1259/bjro.20220026","url":null,"abstract":"<p><p>A wide spectrum of conditions, from life-threatening to non-urgent, can manifest with acute dyspnea, thus presenting major challenges for the treating physician when establishing the diagnosis and severity of the underlying disease. Imaging plays a decisive role in the assessment of acute dyspnea of cardiac and/or pulmonary origin. This article presents an overview of the current imaging modalities used to narrow the differential diagnosis in the assessment of acute dyspnea of cardiac or pulmonary origin. The current indications, findings, accuracy, and limits of each imaging modality are reported. Chest radiography is usually the primary imaging modality applied. There is a low radiation dose associated with this method, and it can assess the presence of fluid in the lung or pleura, consolidations, hyperinflation, pneumothorax, as well as heart enlargement. However, its low sensitivity limits the ability of the chest radiograph to accurately identify the causes of acute dyspnea. CT provides more detailed imaging of the cardiorespiratory system, and therefore, better sensitivity and specificity results, but it is accompanied by higher radiation exposure. Ultrasonography has the advantage of using no radiation, and is fast and feasible as a bedside test and appropriate for the assessment of unstable patients. However, patient-specific factors, such as body habitus, may limit its image quality and interpretability. Advances in knowledge This review provides guidance to the appropriate choice of imaging modalities in the diagnosis of patients with dyspnea of cardiac or pulmonary origin.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"5 1","pages":"20220026"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10077421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9628472","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}