BJR artificial intelligence最新文献

{"title":"Artificial intelligence in radiation therapy: from imaging to delivery-a comprehensive review.","authors":"Kristen Duke, Nikos Papanikolaou","doi":"10.1093/bjrai/ubaf012","DOIUrl":"10.1093/bjrai/ubaf012","url":null,"abstract":"<p><p>Artificial intelligence (AI) is revolutionizing radiotherapy by enhancing precision, efficiency, and personalization in patient care. AI offers innovative solutions to longstanding challenges in radiation oncology by streamlining workflows, improving treatment accuracy, and alleviating the burden of heavy workloads on healthcare professionals, which is particularly critical in addressing current staffing shortages. As AI continues to evolve, its applications in radiotherapy are expected to expand, underscoring the need for healthcare providers to understand its current uses and future potential. This review provides a comprehensive analysis of AI's role across all stages of the radiotherapy workflow, from imaging and treatment planning to quality assurance and delivery. By focusing on recent advancements and key areas of implementation, the review highlights how AI is transforming clinical workflows and improving patient outcomes. Additionally, this review addresses the critical topic of AI regulations and guidelines, which are essential for its safe and ethical integration into clinical practice. It also explores AI's applications in clinical trials, emphasizing its role in enhancing trial efficiency and reliability. By examining these aspects, this review offers a detailed overview of the current state of AI in radiotherapy and its potential to shape the future of cancer treatment.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf012"},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825442","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":"Understanding and leveraging uncertainties in autosegmentation for radiotherapy.","authors":"Stine Sofia Korreman, Jintao Ren","doi":"10.1093/bjrai/ubaf013","DOIUrl":"10.1093/bjrai/ubaf013","url":null,"abstract":"<p><p>This article examines the role of uncertainties in autosegmentation for radiotherapy, which has shown promise in improving consistency, accuracy, and efficiency over traditional manual delineation. Autosegmentation, however, introduces challenges due to uncertainties that arise from factors such as limited training data and models. A nuanced understanding of these uncertainties is essential, as they directly impact clinical decisions and patient outcomes. We explore the sources of uncertainties and how they may be quantified, highlighting the impact on different levels of the segmentation process and discussing the implications of imperfect predictions. Practical applications are proposed, such as uncertainty maps to guide manual adjustments and flagging mechanisms to support quality assurance. We finally address the limitations of these approaches, including computational burdens and risks of information overload. Meaningful uncertainty quantification in autosegmentation holds significant potential to enhance clinical workflows, build trust in artificial intelligence-based tools, and ultimately improve patient care in radiotherapy.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf013"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825585","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-powered personalized patient dosimetry in CT.","authors":"John Damilakis","doi":"10.1093/bjrai/ubaf007","DOIUrl":"10.1093/bjrai/ubaf007","url":null,"abstract":"<p><p>Advanced X-ray imaging techniques have increased the importance of patient dosimetry, requiring better dose assessment methods and improved dose management strategies to optimize imaging protocols, resulting in diagnostic information obtained with the lowest possible dose to the patient. This review explores the potential of artificial intelligence (AI) in CT dosimetry. Using machine learning models, trained with extensive data from personalized Monte Carlo dosimetry, AI significantly enhances the dosimetry process by providing accurate, rapid dose assessments. It automates organ segmentation from CT images, a critical and traditionally time-consuming step, enabling precise estimation of organ doses. AI's ability to predict dose distributions with high accuracy facilitates the seamless integration of personalized dosimetry into clinical workflows, promising improved patient safety and optimized radiation dose management. Additionally, AI-driven approaches offer substantial advancements over conventional methodologies, which often face challenges such as prolonged computation times and labour-intensive manual processes. Despite these advancements, challenges hinder the widespread adoption of AI in CT dosimetry. These include potential biases in training data and the need for robust validation to ensure accuracy across diverse patient populations and imaging conditions. Addressing these challenges is essential for realizing the full potential of AI-powered dosimetry in clinical practice.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf007"},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825440","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":"Impact of artificial intelligence-based and traditional image preprocessing and resampling on MRI-based radiomics for classification of papillary thyroid carcinoma.","authors":"Abdalla Ibrahim, Ramesh Paudyal, Akash Shah, Nora Katabi, Vaios Hatzoglou, Binsheng Zhao, Richard J Wong, Ashok R Shaha, R Michael Tuttle, Lawrence H Schwartz, Amita Shukla-Dave, Aditya Apte","doi":"10.1093/bjrai/ubaf006","DOIUrl":"10.1093/bjrai/ubaf006","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to evaluate the impact of image preprocessing methods, including traditional and artificial intelligence (AI)-based techniques, on the performance of MRI-based radiomics for predicting tumour aggressiveness in papillary thyroid carcinoma (PTC).</p><p><strong>Methods: </strong>We retrospectively analysed MRI data from 69 patients with PTC, acquired between January 2011 and April 2023, alongside corresponding histopathology. MRI scans underwent N4 bias field correction and resampling using 10 traditional methods and an AI-based technique, synthetic multi-orientation resolution enhancement (SMORE). Radiomic features were extracted from the original and preprocessed images. Recursive feature elimination with random forests was used for feature selection, and predictive models were developed using XGBoost. The performance of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC) across 1000 iterations.</p><p><strong>Results: </strong>The combination of the correction of the bias field of N4 with SMORE resampling produced the highest mean AUC (0.75), significantly outperforming all traditional resampling methods ( <math><mrow><mi>P</mi> <mo><</mo> <mn>.001</mn></mrow> </math> ). The lowest mean AUC (0.66) was observed with nearest neighbour resampling. Texture-based radiomic features, particularly the standard deviation of the grey-level co-occurrence matrix autocorrelation, were frequently selected in models using SMORE-resampled images.</p><p><strong>Conclusions: </strong>Preprocessing techniques critically influence the predictive performance of MRI-based radiomics in PTC. The combination of N4 bias field correction and SMORE resampling enhances accuracy, highlighting the necessity of optimizing preprocessing pipelines.</p><p><strong>Advances in knowledge: </strong>This study demonstrates the superiority of AI-driven preprocessing techniques, such as SMORE, in improving MRI radiomic models, paving the way for enhanced clinical decision-making in PTC management.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf006"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12034390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065410","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":"An in-silico simulation study to generate computed tomography images from ultrasound data by using deep learning techniques.","authors":"Anatol A Aicher, Davide Cester, Alexander Martin, Karolina Pawlus, Florian A Huber, Thomas Frauenfelder, Catherine Paverd","doi":"10.1093/bjrai/ubaf005","DOIUrl":"10.1093/bjrai/ubaf005","url":null,"abstract":"<p><strong>Objectives: </strong>Ultrasound has low sensitivity in parenchymal lesion detection compared to contrast-enhanced computed tomography (CT). In this proof-of-concept-study, we investigate whether raw ultrasound data can be used to generate CT-like images using deep learning models in order to enhance lesion detection.</p><p><strong>Methods: </strong>The k-wave ultrasound and Astra CT simulation toolkits were used to generate 2 datasets (1000 samples each) from simulated phantoms with up to 3 inclusions. The pix2pix conditional Generative adversarial network (cGAN) was trained on 800 samples per dataset, reserving the remainder for testing. Outputs were evaluated using generalized contrast-to-noise ratio (gCNR) and Structural Similarity Index (SSIM). Segmentation of B-mode alone versus B-mode with model-generated CT overlay was performed by 2 radiologists (1 board-certified and 1 resident) and both their performance and inter-observer agreement were evaluated using the Jaccard Index.</p><p><strong>Results: </strong>Model-generated CT-like images exhibited significantly improved gCNR ( <math><mrow><mn>0.574</mn> <mo>±</mo> <mn>0.210</mn></mrow> </math> to <math><mrow><mn>0.873</mn> <mo>±</mo> <mn>0.163</mn></mrow> </math> ) and SSIM ( <math><mrow><mn>0.808</mn> <mo>±</mo> <mn>0.092</mn></mrow> </math> to <math><mrow><mn>0.912</mn> <mo>±</mo> <mn>0.062</mn></mrow> </math> ) depending on phantom and inclusion type. Computed tomography-like images sometimes highlighted lesions otherwise undetectable in B-mode. The Jaccard index for 100 test samples improved significantly when segmenting Machine-Learning-augmented B-Mode compared with B-Mode images alone ( <math><mrow><mn>0.58</mn> <mo>±</mo> <mn>0.18</mn></mrow> </math> to <math><mrow><mn>0.69</mn> <mo>±</mo> <mn>0.16</mn></mrow> </math> ), depending on dataset and radiologist. Inter-observer agreement also improved significantly ( <math><mrow><mn>0.74</mn> <mo>±</mo> <mn>0.18</mn></mrow> </math> to <math><mrow><mn>0.85</mn> <mo>±</mo> <mn>0.07</mn></mrow> </math> for 1 dataset).</p><p><strong>Conclusions: </strong>Deep learning models can effectively translate ultrasound data into CT-like images, improving quality and inter-observer agreement, and enhancing lesion detectability, for example, by alleviating shadowing artefacts.</p><p><strong>Advances in knowledge: </strong>Generating CT-like images using raw ultrasound RF data as input to a cGAN model results in a significant improvement in lesion detectability by, for example, alleviating acoustic shadowing. With a cGAN architecture, even relatively small datasets can successfully generate CT-like images that improve lesion detectability.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf005"},"PeriodicalIF":0.0,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825454","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 and consistency in patient care: a large-scale longitudinal study of mammographic density assessment.","authors":"Susan O Holley, Daniel Cardoza, Thomas P Matthews, Elisha E Tibatemwa, Rodrigo Morales Hoil, Adetunji T Toriola, Aimilia Gastounioti","doi":"10.1093/bjrai/ubaf004","DOIUrl":"10.1093/bjrai/ubaf004","url":null,"abstract":"<p><strong>Objectives: </strong>To assess whether use of an artificial intelligence (AI) model for mammography could result in more longitudinally consistent breast density assessments compared with interpreting radiologists.</p><p><strong>Methods: </strong>The AI model was evaluated retrospectively on a large mammography dataset including 50 sites across the United States from an outpatient radiology practice. Examinations were acquired on Hologic imaging systems between 2016 and 2021 and were interpreted by 39 radiologists (36% fellowship trained; years of experience: 2-37 years). Longitudinal patterns in 4-category breast density and binary breast density (non-dense vs. dense) were characterized for all women with at least 3 examinations (61 177 women; 214 158 examinations) as constant, descending, ascending, or bi-directional. Differences in longitudinal density patterns were assessed using paired proportion hypothesis testing.</p><p><strong>Results: </strong>The AI model produced more constant (<i>P</i> < .001) and fewer bi-directional (<i>P</i> < .001) longitudinal density patterns compared to radiologists (AI: constant 81.0%, bi-directional 4.9%; radiologists: constant 56.8%, bi-directional 15.3%). The AI density model also produced more constant (<i>P</i> < .001) and fewer bi-directional (<i>P</i> < .001) longitudinal patterns for binary breast density. These findings held in various subset analyses, which minimize (1) change in breast density (post-menopausal women, women with stable image-based BMI), (2) inter-observer variability (same radiologist), and (3) variability by radiologist's training level (fellowship-trained radiologists).</p><p><strong>Conclusions: </strong>AI produces more longitudinally consistent breast density assessments compared with interpreting radiologists.</p><p><strong>Advances in knowledge: </strong>Our results extend the advantages of AI in breast density evaluation beyond automation and reproducibility, showing a potential path to improved longitudinal consistency and more consistent downstream care for screened women.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf004"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812933","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":"Educational insights of artificial intelligence assisted chest radiograph interpretation in lung cancer detection-pictorial review.","authors":"Jenna R Tugwell-Allsup, Andrew England, Bethan W Owen","doi":"10.1093/bjrai/ubaf002","DOIUrl":"10.1093/bjrai/ubaf002","url":null,"abstract":"<p><p>Chest radiographs (CXRs) are one of the most complicated, yet most frequently performed imaging examinations worldwide, and are susceptible to reporting discrepancies, especially in the detection of lung cancer (LC). The use of artificial intelligence (AI) as a decision-supporting tool for CXRs has been shown to aid earlier LC detection and reduce reporting discrepancies. Nevertheless, the use of AI within radiology is still in its infancy in terms of clinical implementation. There is a need for more pragmatic evidence to enhance key stakeholders' perceptions and understating of AI within clinical practice. Most AI studies focus on comparing sensitivity, specificity, and accuracy against a re-review of pre-selected images by radiologists, often paired with complex technical data and statistical analyses. Improved understanding and awareness of AI capabilities whilst learning from AI discrepancies could help optimize human-AI interactions and acceptance within clinical practice whilst informing future research. The aim of this educational review is to provide a visual portrayal of real-world examples of missed LC cases from within a retrospective AI study, explore the impact of image quality on AI performance, and highlight cases of AI discrepancies.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf002"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825417","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 AI doctor will see you now: public perspectives on artificial intelligence in healthcare.","authors":"Carolyn Horst, Muhammad Aniq, Alice Taylor-Gee, Jennifer Wong, Vicky Goh","doi":"10.1093/bjrai/ubaf003","DOIUrl":"10.1093/bjrai/ubaf003","url":null,"abstract":"<p><strong>Objectives: </strong>The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, that is, current and future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there are only limited data on the public's views. The aim of this study is to understand the public's perspective on the use of AI in healthcare.</p><p><strong>Methods: </strong>An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire contained 8 questions based on previously validated subject areas designed to assess respondents' views on the use of AI in healthcare. Brief demographic data were also collected.</p><p><strong>Results: </strong>The population surveyed was more diverse and younger than the general UK population (64% White, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. Seventy-one percent did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings.</p><p><strong>Conclusions: </strong>Our study shows that the subset of the general public we surveyed, largely comprised of young, likely future healthcare users, is comfortable with the use of AI in healthcare, but does not see it as a replacement for doctors.</p><p><strong>Advances in knowledge: </strong>This article highlights views from a subset of the general public on the use of AI in healthcare, which is largely under researched.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf003"},"PeriodicalIF":0.0,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825528","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 assisted detection of large vessel occlusion on CT angiography in acute stroke patients: a multi-reader multi-case study.","authors":"Kiruba Nagaratnam, Phil Mathieson, Anna Podlasek, Peter Slade, Gary A Ford, Anthony Cox, James H Briggs, Zoe V J Woodhead, George Harston","doi":"10.1093/bjrai/ubaf001","DOIUrl":"10.1093/bjrai/ubaf001","url":null,"abstract":"<p><strong>Objectives: </strong>We assessed the impact of artificial intelligence (AI) software (e-CTA, Brainomix) on clinical decision-making in patients with suspected acute ischemic stroke.</p><p><strong>Methods: </strong>A retrospective, multi-reader-multi-case crossover design compared readers' performance with vs without software support. Twenty cases were included, 10 with large vessel occlusion (LVO) and 10 without LVO. Twenty-one NHS clinicians, representing intended software users ranging in experience, conducted 2 sessions (washout period > 2 weeks). In session 1, software support was provided for 10 randomly selected cases. In session 2, support allocation was reversed. Outcome measures included LVO detection, collateral scoring, diagnosis, treatment decision, time taken and confidence.</p><p><strong>Results: </strong>Sensitivity, specificity, and accuracy of LVO detection improved with imaging software for LVO detection, with increased confidence and reduced time taken. There was no significant difference in collateral scoring or diagnoses.</p><p><strong>Conclusion: </strong>e-CTA can improve performance of NHS clinicians when interpreting acute stroke imaging.</p><p><strong>Advances in knowledge: </strong>This paper provides new evidence that AI decision support software has the capacity to improve the performance of representative users in the NHS when interpreting imaging to identify patients for acute stroke treatments.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubaf001"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825464","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":"Multimodal artificial intelligence models for radiology.","authors":"Amara Tariq, Imon Banerjee, Hari Trivedi, Judy Gichoya","doi":"10.1093/bjrai/ubae017","DOIUrl":"10.1093/bjrai/ubae017","url":null,"abstract":"<p><p>Artificial intelligence (AI) models in medicine often fall short in real-world deployment due to inability to incorporate multiple data modalities in their decision-making process as clinicians do. Clinicians integrate evidence and signals from multiple data sources like radiology images, patient clinical status as recorded in electronic health records, consultations from fellow providers, and even subtle clues using the appearance of a patient, when making decisions about diagnosis or treatment. To bridge this gap, significant research effort has focused on building fusion models capable of harnessing multi-modal data for advanced decision making. We present a broad overview of the landscape of research in multimodal AI for radiology covering a wide variety of approaches from traditional fusion modelling to modern vision-language models. We provide analysis of comparative merits and drawbacks of each approach to assist future research and highlight ethical consideration in developing multimodal AI. In practice, the quality and quantity of available training data, availability of computational resources, and clinical application dictates which fusion method may be most suitable.</p>","PeriodicalId":517427,"journal":{"name":"BJR artificial intelligence","volume":"2 1","pages":"ubae017"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825560","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}