Clinical Imaging最新文献

{"title":"Evaluating ChatGPT's performance across radiology subspecialties: A meta-analysis of board-style examination accuracy and variability","authors":"Dan Nguyen ,&nbsp;Grace Hyun J. Kim ,&nbsp;Arash Bedayat","doi":"10.1016/j.clinimag.2025.110551","DOIUrl":"10.1016/j.clinimag.2025.110551","url":null,"abstract":"<div><h3>Introduction</h3><div>Large language models (LLMs) like ChatGPT are increasingly used in medicine due to their ability to synthesize information and support clinical decision-making. While prior research has evaluated ChatGPT's performance on medical board exams, limited data exist on radiology-specific exams especially considering prompt strategies and input modalities. This meta-analysis reviews ChatGPT's performance on radiology board-style questions, assessing accuracy across radiology subspecialties, prompt engineering methods, GPT model versions, and input modalities.</div></div><div><h3>Methods</h3><div>Searches in PubMed and SCOPUS identified 163 articles, of which 16 met inclusion criteria after excluding irrelevant topics and non-board exam evaluations. Data extracted included subspecialty topics, accuracy, question count, GPT model, input modality, prompting strategies, and access dates. Statistical analyses included two-proportion z-tests, a binomial generalized linear model (GLM), and meta-regression with random effects (Stata v18.0, R v4.3.1).</div></div><div><h3>Results</h3><div>Across 7024 questions, overall accuracy was 58.83 % (95 % CI, 55.53–62.13). Performance varied widely by subspecialty, highest in emergency radiology (73.00 %) and lowest in musculoskeletal radiology (49.24 %). GPT-4 and GPT-4o significantly outperformed GPT-3.5 (<em>p</em> &lt; .001), but visual inputs yielded lower accuracy (46.52 %) compared to textual inputs (67.10 %, <em>p</em> &lt; .001). Prompting strategies showed significant improvement (<em>p</em> &lt; .01) with basic prompts (66.23 %) compared to no prompts (59.70 %). A modest but significant decline in performance over time was also observed (<em>p</em> &lt; .001).</div></div><div><h3>Discussion</h3><div>ChatGPT demonstrates promising but inconsistent performance in radiology board-style questions. Limitations in visual reasoning, heterogeneity across studies, and prompt engineering variability highlight areas requiring targeted optimization.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110551"},"PeriodicalIF":1.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The diagnostic performance of dual-energy CT in detecting chronic thromboembolic pulmonary hypertension: A systematic-review and Meta-analysis","authors":"Parya Valizadeh ,&nbsp;Payam Jannatdoust ,&nbsp;Shayan Shojaei ,&nbsp;Asma Mousavi ,&nbsp;Ali Gholamrezanezhad","doi":"10.1016/j.clinimag.2025.110552","DOIUrl":"10.1016/j.clinimag.2025.110552","url":null,"abstract":"<div><h3>Background and aims</h3><div>Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe, treatable condition often underdiagnosed due to nonspecific symptoms. Dual-Energy Computed Tomography (DECT) shows promise in the detailed assessment of pulmonary perfusion, providing quantitative values such as perfused blood volume (PBV) and iodine density (ID), potentially helpful in detecting CTEPH. This meta-analysis evaluates the diagnostic accuracy of DECT in detecting CTEPH and its potential role in clinical management.</div></div><div><h3>Methods</h3><div>Following PRISMA guidelines, a literature search was conducted in PubMed, Web of Science, Scopus, and Embase up to June 2024. Studies providing diagnostic accuracy data for DECT in CTEPH were included. Data were aggregated using a bivariate model in the R statistical programming environment.</div></div><div><h3>Results</h3><div>Nine studies with 751 participants were included. The pooled sensitivity and specificity of DECT for detecting CTEPH were 87.5 % (95 % CI: 74.8–94.2 %) and 91.2 % (95 % CI: 84.1–95.3 %), with an AUC of 0.95 (95 % CI: 0.87–0.97). The diagnostic accuracy did not significantly differ when distinguishing CTEPH from acute pulmonary thromboembolism (APTE) or non-thromboembolic conditions (<em>p</em> = 0.781). Subgroup analyses based on different quantitative indices and patient vs. segment-based assessments showed no significant differences. Heterogeneity was high, and the risk of bias assessment identified concerns regarding patient selection.</div></div><div><h3>Conclusion</h3><div>DECT shows promising diagnostic accuracy in detecting CTEPH. However, its suboptimal sensitivity and variability in protocols pose challenges. Future research should focus on identifying optimal diagnostic criteria, indices, and thresholds to standardize DECT use in clinical practice for broader applicability.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110552"},"PeriodicalIF":1.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance assessment of an artificial intelligence algorithm for opportunistic screening of abdominal aortic aneurysms","authors":"Danling Chen ,&nbsp;Mark Krycia ,&nbsp;Jerome Avondo ,&nbsp;Joseph Cavallo","doi":"10.1016/j.clinimag.2025.110550","DOIUrl":"10.1016/j.clinimag.2025.110550","url":null,"abstract":"<div><h3>Purpose</h3><div>Abdominal aortic aneurysm (AAA) is a common incidental finding on CT imaging performed in the acute care setting. Artificial intelligence (AI) algorithms have been developed to automatically measure aortic lumen size and thus facilitate AAA detection. However, few studies have evaluated the performance of such tools in a large clinical setting. This retrospective study aimed to evaluate the performance of a commercially-available AI algorithm for the opportunistic screening of incidental AAA on non-optimized CT imaging.</div></div><div><h3>Methods</h3><div>CT examinations of the abdomen and pelvis performed in the emergency setting of a tertiary academic center between July 2020 and May 2021 were retrospectively processed by the AI algorithm, while natural language processing software (NLP) was used to analyze the initial radiology report. Exams which were positive for the presence of AAA on imaging by AI analysis, but negative by NLP of their corresponding report, were designated as potential discrepancies and independently reviewed by an ED radiologist.</div></div><div><h3>Results</h3><div>4023 abdominal and pelvic CT examinations were analyzed. 98.3 % (3955) cases were negative for presence of AAA by NLP assessment of their respective report, with 16 of these cases flagged by AI as discrepancies potentially positive for AAA. 31 % (5/16) of these cases were determined by secondary review to be truly positive for previously undocumented AAA. The enhanced detection rate with AI assistance was 7.4 %.</div></div><div><h3>Discussion</h3><div>Artificial intelligence algorithms demonstrate the potential to improve detection rates of incidental abdominal aortic aneurysms on CT imaging, particularly in high throughput workflows such as the emergency department.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110550"},"PeriodicalIF":1.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based colorectal cancer detection in medical images: A comprehensive analysis of datasets, methods, and future directions","authors":"Burak Gülmez","doi":"10.1016/j.clinimag.2025.110542","DOIUrl":"10.1016/j.clinimag.2025.110542","url":null,"abstract":"<div><div>This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality publications and 9 publicly accessible medical image datasets used for training and validation. Various convolutional neural network architectures—including ResNet (40 implementations), VGG (18 implementations), and emerging transformer-based models (12 implementations)—for classification, object detection, and segmentation tasks are systematically categorized and evaluated. The investigation encompasses hyperparameter optimization techniques utilized to enhance model performance, with particular focus on genetic algorithms and particle swarm optimization approaches. The role of explainable AI methods in medical diagnosis interpretation is analyzed through visualization techniques such as Grad-CAM and SHAP. Technical limitations, including dataset scarcity, computational constraints, and standardization challenges, are identified through trend analysis. Research gaps in current methodologies are highlighted through comparative assessment of performance metrics across different architectural implementations. Potential future research directions, including multimodal learning and federated learning approaches, are proposed based on publication trend analysis. This review serves as a comprehensive reference for researchers in medical image analysis and clinical practitioners implementing AI-based colorectal cancer detection systems.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110542"},"PeriodicalIF":1.8,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Appropriateness of acute breast symptom recommendations provided by ChatGPT","authors":"Clifton Byrd ,&nbsp;Chase Kingsbury ,&nbsp;Bethany Niell ,&nbsp;Kimberly Funaro ,&nbsp;Asha Bhatt ,&nbsp;R. Jared Weinfurtner ,&nbsp;Dana Ataya","doi":"10.1016/j.clinimag.2025.110549","DOIUrl":"10.1016/j.clinimag.2025.110549","url":null,"abstract":"<div><h3>Purpose</h3><div>We evaluated the accuracy of ChatGPT-3.5's responses to common questions regarding acute breast symptoms and explored whether using lay language, as opposed to medical language, affected the accuracy of the responses.</div></div><div><h3>Methods</h3><div>Questions were formulated addressing acute breast conditions, informed by the American College of Radiology (ACR) Appropriateness Criteria (AC) and our clinical experience at a tertiary referral breast center. Of these, seven addressed the most common acute breast symptoms, nine addressed pregnancy-associated breast symptoms, and four addressed specific management and imaging recommendations for a palpable breast abnormality. Questions were submitted three times to ChatGPT-3.5 and all responses were assessed by five fellowship-trained breast radiologists. Evaluation criteria included clinical judgment and adherence to the ACR guidelines, with responses scored as: 1) “appropriate,” 2) “inappropriate” if any response contained inappropriate information, or 3) “unreliable” if responses were inconsistent. A majority vote determined the appropriateness for each question.</div></div><div><h3>Results</h3><div>ChatGPT-3.5 generated responses were appropriate for 7/7 (100 %) questions regarding common acute breast symptoms when phrased both colloquially and using standard medical terminology. In contrast, ChatGPT-3.5 generated responses were appropriate for 3/9 (33 %) questions about pregnancy-associated breast symptoms and 3/4 (75 %) questions about management and imaging recommendations for a palpable breast abnormality.</div></div><div><h3>Conclusion</h3><div>ChatGPT-3.5 can automate healthcare information related to appropriate management of acute breast symptoms when prompted with both standard medical terminology or lay phrasing of the questions. However, physician oversight remains critical given the presence of inappropriate recommendations for pregnancy associated breast symptoms and management of palpable abnormalities.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110549"},"PeriodicalIF":1.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel investigational modalities for clinical breast imaging: A narrative review","authors":"Shivangi Sengupta ,&nbsp;Julie Lang ,&nbsp;Cherie M. Kuzmiak","doi":"10.1016/j.clinimag.2025.110548","DOIUrl":"10.1016/j.clinimag.2025.110548","url":null,"abstract":"<div><div>Breast cancer detection is currently performed using the gold standards of mammography, conventional handheld ultrasound, and MRI. However, each of these modalities presents notable limitations: mammography has reduced sensitivity in dense breast tissue; most conventional handheld ultrasound is subjective and lacks quantitative tissue characterization; and MRI involves longer scan times and lower patient comfort compared to other methods. Therefore, exploring alternative breast imaging modalities is essential. This narrative review evaluates three emerging technologies in clinical breast imaging—multimodal ultrasound tomography, optoacoustic imaging, and fusion imaging—focusing on their underlying technologies, clinical potential, and comparisons to current imaging standards.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110548"},"PeriodicalIF":1.8,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to letter concerning publication ‘Diversity, inclusivity and traceability of mammography datasets used in development of artificial intelligence technologies: a systematic review’","authors":"Xiaoxuan Liu,&nbsp;Elinor Laws","doi":"10.1016/j.clinimag.2025.110539","DOIUrl":"10.1016/j.clinimag.2025.110539","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"126 ","pages":"Article 110539"},"PeriodicalIF":1.5,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient perspectives on AI in radiology: Insights from the United Arab Emirates","authors":"Mohamed Zakaria El-Sayed ,&nbsp;Mohammad Rawashdeh ,&nbsp;Aysha Moossa ,&nbsp;Maryam Atfah ,&nbsp;B. Prajna ,&nbsp;Magdi.A. Ali","doi":"10.1016/j.clinimag.2025.110543","DOIUrl":"10.1016/j.clinimag.2025.110543","url":null,"abstract":"<div><h3>Rationale and objectives</h3><div>Artificial intelligence (AI) enhances diagnostic accuracy, efficiency, and patient outcomes in radiology. Patient acceptance is essential for successful integration. This study examines patient perspectives on AI in radiology within the UAE, focusing on their knowledge, attitudes, and perceived barriers. Understanding these factors can address concerns, improve trust, and guide patient-centered AI implementation. The findings aim to support effective AI adoption in healthcare.</div></div><div><h3>Methods</h3><div>A cross-sectional study involving 205 participants undergoing radiological imaging in the UAE. Data was collected through an online questionnaire, developed based on a literature review, and pre-tested for reliability and validity. Non-probability sampling methods, including convenience and snowball sampling, were employed. The questionnaire assessed participants' knowledge, attitudes, and perceived barriers regarding AI in radiology. Data was analyzed, and categorical variables were expressed as frequencies and percentages.</div></div><div><h3>Results</h3><div>Most participants (89.8 %) believed AI could improve diagnostic accuracy, and 87.8 % acknowledged its role in prioritizing urgent cases. However, only 22 % had direct experience with AI in radiology. While 81 % expressed comfort with AI-based technology, concerns about data security (80.5 %), lack of empathy in AI systems (82.9 %), and insufficient information about AI (85.8 %) were significant barriers. Additionally, (87.3 %) of participants were concerned about the cost of AI implementation. Despite these concerns, 86.3 % believed AI could improve the quality of radiological services, and 83.9 % were satisfied with its potential applications.</div></div><div><h3>Conclusion</h3><div>UAE patients generally support AI in radiology, recognizing its potential for improved diagnostic accuracy. However, concerns about data security, empathy, and understanding of AI technologies necessitate improved patient education, transparent communication, and regulatory frameworks to foster trust and acceptance.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110543"},"PeriodicalIF":1.8,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phyllodes tumor of the breast: Race-related differences in presentation, pathology and prognosis","authors":"Natalia Eugene ,&nbsp;Eric Li ,&nbsp;Julianna Wolochuk ,&nbsp;Courtney Lee ,&nbsp;Zi Zhang","doi":"10.1016/j.clinimag.2025.110544","DOIUrl":"10.1016/j.clinimag.2025.110544","url":null,"abstract":"<div><h3>Purpose</h3><div>We aimed to investigate the race-related differences in clinical, radiological, and pathological presentation and prognosis of phyllodes tumors (PTs).</div></div><div><h3>Methods</h3><div>This retrospective cohort study included patients diagnosed with PTs from 5/1/2012 to 5/31/2022. Pathology reports, radiology findings, and clinical data were extracted from electronic medical records. Statistical analysis assessed these differences across racial demographics.</div></div><div><h3>Results</h3><div>Among 62 women with PTs (mean age: 41 ± 15.2 years), the racial distribution was 21 % Caucasian, 32 % African American, 17.7 % Hispanic, and 29 % Asian/Other. Minority women were significantly more likely to present with palpable masses than Caucasian patients (<em>p</em> = 0.031), suggesting potential disparities in early detection. Minority patients were more likely to be covered by Medicaid and reside in lower-income ZIP codes, with a higher proportion under age 40 at presentation, though these results were not statistically significant. While tumor size varied by race, this difference was also not statistically significant (<em>p</em> = 0.094). Pathologically, 66.1 % of tumors were benign, 22.6 % borderline, and 11.3 % malignant. Although lumpectomy was the preferred approach across all racial groups, no Caucasian or Hispanic patients undergoing mastectomy. No significant racial differences were observed in resection margins (<em>p</em> = 0.263), re-excision rates (<em>p</em> = 0.503), or recurrence rates (<em>p</em> = 0.238).</div></div><div><h3>Conclusion</h3><div>Minority women had a higher likelihood of presenting with symptomatic PTs, underscoring potential disparities in screening. While treatment outcomes were similar across racial groups, targeted screening efforts in at-risk populations may improve early detection and promote equitable healthcare access.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110544"},"PeriodicalIF":1.8,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is 18F-FDG-PET/CT useful in staging of locally advanced signet-ring cell esophageal adenocarcinoma?","authors":"Sonia L. Betancourt Cuellar ,&nbsp;Marcelo F. Benveniste ,&nbsp;Diana Palacio ,&nbsp;Afaf Atiyah ,&nbsp;Wayne L. Hofstetter ,&nbsp;Jeremy J. Erasmus","doi":"10.1016/j.clinimag.2025.110545","DOIUrl":"10.1016/j.clinimag.2025.110545","url":null,"abstract":"<div><h3>Objective</h3><div>Signet-ring cell esophageal carcinoma (SRCEC) is a subtype of adenocarcinoma in which &gt;10 %–50 % of cells have intracellular mucin. There is limited information regarding PET/CT in staging SRCEC. The purpose of this study is to evaluate the usefulness of PET/CT in staging patients with locally advanced SRCEC.</div></div><div><h3>Methods</h3><div>91 patients with biopsy-proven SRCEC and pretreatment FDG-PET/CT were included. SUVmax of the primary tumor, and size and SUVmax of regional lymph nodes were evaluated. In patients who had presurgical FDG-PET/CT after neoadjuvant therapy, response of the primary tumor and nodal metastases were assessed.</div></div><div><h3>Results</h3><div>Primary tumor was avid in 80/91 (88 %) (SUVmax range 3.8–28). Stage of 11 tumors without FDG uptake was T1N0–1 (<em>n</em> = 3), T2N0 (<em>n</em> = 1) and T3N0–2 (<em>n</em> = 7). 68 (75 %) patients had non-FDG avid nodes. Biopsy in 32/68 patients with non-FDG avid nodes was positive in 12 (38 %). 23 had FDG avid nodes, 14 were biopsied and 3 were positive for metastases.</div><div>After chemoradiation, 73 (86 %) had persistent FDG uptake in the primary tumor (SUVmax range 3.5–17.9) and 68 (93 %) had residual viable malignancy. 8/11 patients with resolution of FDG uptake in the primary tumor had persistent malignancy.</div><div>Regarding nodal metastases, 41/84 (49 %) had residual nodal disease in surgical specimens and only 4 (10 %) had nodes that were FDG avid.</div></div><div><h3>Conclusion</h3><div>PET/CT is useful in detecting the primary tumor in patients with SRCEC during the initial staging and after neoadjuvant therapy. The utility of PE/CT in detecting locoregional nodal disease prior to and after chemoradiation is limited.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110545"},"PeriodicalIF":1.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}