Frontiers in Artificial Intelligence最新文献

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Editorial: Explainable AI in Natural Language Processing. 社论:自然语言处理中的可解释人工智能。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1472086
Somnath Banerjee, David Tomás
{"title":"Editorial: Explainable AI in Natural Language Processing.","authors":"Somnath Banerjee, David Tomás","doi":"10.3389/frai.2024.1472086","DOIUrl":"https://doi.org/10.3389/frai.2024.1472086","url":null,"abstract":"","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1472086"},"PeriodicalIF":3.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142112828","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}
引用次数: 0
Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation. 加强中医药 RCT 证据提取的自动信息提取模型(Evi-BERT):算法开发与验证。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-15 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1454945
Yizhen Li, Zhongzhi Luan, Yixing Liu, Heyuan Liu, Jiaxing Qi, Dongran Han
{"title":"Automated information extraction model enhancing traditional Chinese medicine RCT evidence extraction (Evi-BERT): algorithm development and validation.","authors":"Yizhen Li, Zhongzhi Luan, Yixing Liu, Heyuan Liu, Jiaxing Qi, Dongran Han","doi":"10.3389/frai.2024.1454945","DOIUrl":"https://doi.org/10.3389/frai.2024.1454945","url":null,"abstract":"<p><strong>Background: </strong>In the field of evidence-based medicine, randomized controlled trials (RCTs) are of critical importance for writing clinical guidelines and providing guidance to practicing physicians. Currently, RCTs rely heavily on manual extraction, but this method has data breadth limitations and is less efficient.</p><p><strong>Objectives: </strong>To expand the breadth of data and improve the efficiency of obtaining clinical evidence, here, we introduce an automated information extraction model for traditional Chinese medicine (TCM) RCT evidence extraction.</p><p><strong>Methods: </strong>We adopt the Evidence-Bidirectional Encoder Representation from Transformers (Evi-BERT) for automated information extraction, which is combined with rule extraction. Eleven disease types and 48,523 research articles from the China National Knowledge Infrastructure (CNKI), WanFang Data, and VIP databases were selected as the data source for extraction. We then constructed a manually annotated dataset of TCM clinical literature to train the model, including ten evidence elements and 24,244 datapoints. We chose two models, BERT-CRF and BiLSTM-CRF, as the baseline, and compared the training effects with Evi-BERT and Evi-BERT combined with rule expression (RE).</p><p><strong>Results: </strong>We found that Evi-BERT combined with RE achieved the best performance (precision score = 0.926, Recall = 0.952, F1 score = 0.938) and had the best robustness. We totally summarized 113 pieces of rule datasets in the regulation extraction procedure. Our model dramatically expands the amount of data that can be searched and greatly improves efficiency without losing accuracy.</p><p><strong>Conclusion: </strong>Our work provided an intelligent approach to extracting clinical evidence for TCM RCT data. Our model can help physicians reduce the time spent reading journals and rapidly speed up the screening of clinical trial evidence to help generate accurate clinical reference guidelines. Additionally, we hope the structured clinical evidence and structured knowledge extracted from this study will help other researchers build large language models in TCM.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1454945"},"PeriodicalIF":3.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142112752","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}
引用次数: 0
Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis. 医疗物联网中人工智能的文献计量分析:绘图和性能分析。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-12 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1347815
Haruna Chiroma, Ibrahim Abaker Targio Hashem, Mohammed Maray
{"title":"Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis.","authors":"Haruna Chiroma, Ibrahim Abaker Targio Hashem, Mohammed Maray","doi":"10.3389/frai.2024.1347815","DOIUrl":"10.3389/frai.2024.1347815","url":null,"abstract":"<p><p>The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1347815"},"PeriodicalIF":3.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11345150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074057","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}
引用次数: 0
A methodology for planning, implementation and evaluation of skills intelligence management - results of a design science project in technology organisations. 技能智能管理的规划、实施和评估方法--技术组织设计科学项目的成果。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-07 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1424924
Kadri-Liis Kusmin, Peeter Normak, Tobias Ley
{"title":"A methodology for planning, implementation and evaluation of skills intelligence management - results of a design science project in technology organisations.","authors":"Kadri-Liis Kusmin, Peeter Normak, Tobias Ley","doi":"10.3389/frai.2024.1424924","DOIUrl":"10.3389/frai.2024.1424924","url":null,"abstract":"<p><strong>Introduction: </strong>The evolving labour market requirements amidst digital transformation necessitate robust skills intelligence for informed decision-making and adaptability. Novel technologies such as Big Data, Machine Learning, and Artificial Intelligence have significant potential for enhancing skills intelligence.</p><p><strong>Methods: </strong>This study bridges the gap between theory and practice by designing a novel software artefact for skills intelligence management. With its systematic framework for identifying skills intelligence elements, an assessment instrument, and an implementation methodology, the artefact ensures a thorough approach to skills intelligence management.</p><p><strong>Results: </strong>The artefact was demonstrated in 11 organisations. Feedback collected from interviews, focus group sessions, and observations (<i>N</i> = 19) indicated that the artefact is a feasible starting point for implementing or systematising skills intelligence management. Participants suggested improvements but concurred that the systematic approach enhances skills intelligence data collection and quality.</p><p><strong>Discussion: </strong>The study shows that the artefact facilitates the application of advanced technologies in skills intelligence management. Additionally, it contributes a set of principles for effective skills intelligence management, fostering a broader conversation on this critical topic. Participants' feedback underscores the artefact's potential and provides a basis for further refinement and application in diverse organisational contexts.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1424924"},"PeriodicalIF":3.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11335683/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142018913","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}
引用次数: 0
Enhancing the design of voting advice applications with BERT language model. 利用 BERT 语言模型改进投票建议应用程序的设计。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-06 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1343214
Daniil Buryakov, Mate Kovacs, Uwe Serdült, Victor Kryssanov
{"title":"Enhancing the design of voting advice applications with BERT language model.","authors":"Daniil Buryakov, Mate Kovacs, Uwe Serdült, Victor Kryssanov","doi":"10.3389/frai.2024.1343214","DOIUrl":"10.3389/frai.2024.1343214","url":null,"abstract":"<p><p>The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1343214"},"PeriodicalIF":3.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009595","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}
引用次数: 0
Self-attention with temporal prior: can we learn more from the arrow of time? 有时间先验的自我关注:我们能从时间之箭中学到更多吗?
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-06 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1397298
Kyung Geun Kim, Byeong Tak Lee
{"title":"Self-attention with temporal prior: can we learn more from the arrow of time?","authors":"Kyung Geun Kim, Byeong Tak Lee","doi":"10.3389/frai.2024.1397298","DOIUrl":"10.3389/frai.2024.1397298","url":null,"abstract":"<p><p>Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that <i>interrelations</i> of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible. This is because, while they are good at learning piece-wise temporal dependencies, attention-based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode the short-term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. We chose various prediction tasks for the experiments using Electronic Health Records (EHR) data sets since they are great examples with underlying long- and short-term temporal dependencies. Our experiments show exceptional classification results compared to best-performing models on most tasks and data sets.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1397298"},"PeriodicalIF":3.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009596","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}
引用次数: 0
A comparison of the diagnostic ability of large language models in challenging clinical cases. 比较大型语言模型在具有挑战性的临床病例中的诊断能力。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-05 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1379297
Maria Palwasha Khan, Eoin Daniel O'Sullivan
{"title":"A comparison of the diagnostic ability of large language models in challenging clinical cases.","authors":"Maria Palwasha Khan, Eoin Daniel O'Sullivan","doi":"10.3389/frai.2024.1379297","DOIUrl":"10.3389/frai.2024.1379297","url":null,"abstract":"<p><strong>Introduction: </strong>The rise of accessible, consumer facing large language models (LLM) provides an opportunity for immediate diagnostic support for clinicians.</p><p><strong>Objectives: </strong>To compare the different performance characteristics of common LLMS utility in solving complex clinical cases and assess the utility of a novel tool to grade LLM output.</p><p><strong>Methods: </strong>Using a newly developed rubric to assess the models' diagnostic utility, we measured to models' ability to answer cases according to accuracy, readability, clinical interpretability, and an assessment of safety. Here we present a comparative analysis of three LLM models-Bing, Chat GPT, and Gemini-across a diverse set of clinical cases as presented in the New England Journal of Medicines case series.</p><p><strong>Results: </strong>Our results suggest that models performed differently when presented with identical clinical information, with Gemini performing best. Our grading tool had low interobserver variability and proved a reliable tool to grade LLM clinical output.</p><p><strong>Conclusion: </strong>This research underscores the variation in model performance in clinical scenarios and highlights the importance of considering diagnostic model performance in diverse clinical scenarios prior to deployment. Furthermore, we provide a new tool to assess LLM output.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1379297"},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005445","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}
引用次数: 0
A framework for extending co-creative communication models to sustainability research. 将共同创造交流模式扩展到可持续发展研究的框架。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-05 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1236310
Guanhong Li, Xiaoyun Guo
{"title":"A framework for extending co-creative communication models to sustainability research.","authors":"Guanhong Li, Xiaoyun Guo","doi":"10.3389/frai.2024.1236310","DOIUrl":"10.3389/frai.2024.1236310","url":null,"abstract":"<p><p>The UN Sustainable Development Goals (SDGs) present a challenge due to their potential for conflicting objectives, which hinders their effective implementation. In order to address the complexity of sustainability issues, a framework capable of capturing the specificity of diverse sustainability issues while offering a common methodology applicable across contexts is required. Co-creative communication can be regarded as a key source of uncertainty within functional systems, as it can be instrumental in realizing and sustaining sustainability. In this regard, the studies in Constructive approaches to Co-creative Communication (CCC), particularly those employing artificial intelligence (AI) methodologies such as computational social science and innovation studies, hold significant value for both theoretical and applied sustainability research. However, existing CCC frameworks cannot be directly applied to sustainability research. This work bridges this gap by proposing a framework that outlines a general approach to establishing formalized definitions of sustainability from the lens of communication. This approach enables the direct application of CCC models to sustainability studies. The framework is based on systems theory and the methodologies of artificial intelligence, including computational/symbolic modeling and formal methods. This framework emphasizes the social function of co-creative communication and the interaction between the innovation process and the sustainability of the system. It can be concluded that the application of our framework enables the achievements of CCC to be directly applied to sustainability research. Researchers from different disciplines are therefore able to establish their own specific definitions of sustainability, which are tailored to their particular concerns. Our framework lays the groundwork for future sustainability studies that employs CCC, facilitating the integration of CCC insights into sustainability research and application. The outcomes of computational creativity research based on AI technologies, such as distributed artificial intelligence and self-organizing networks, can deepen the understanding of sustainability mechanisms and drive their practical applications. Furthermore, the functional role of co-creative communication in societal sustainability proposed in this work offers a novel perspective for future discussions on the evolutionary adaptation of co-creative communication.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1236310"},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005446","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}
引用次数: 0
Enhanced fingerprint classification through modified PCA with SVD and invariant moments. 利用 SVD 和不变矩的修正 PCA 增强指纹分类。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-05 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1433494
Ala Balti, Abdelaziz Hamdi, Sabeur Abid, Mohamed Moncef Ben Khelifa, Mounir Sayadi
{"title":"Enhanced fingerprint classification through modified PCA with SVD and invariant moments.","authors":"Ala Balti, Abdelaziz Hamdi, Sabeur Abid, Mohamed Moncef Ben Khelifa, Mounir Sayadi","doi":"10.3389/frai.2024.1433494","DOIUrl":"10.3389/frai.2024.1433494","url":null,"abstract":"<p><p>This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1433494"},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005447","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}
引用次数: 0
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke. 基于入院 CT 血管造影预测大血管闭塞性卒中血栓切除术后预后的深度学习。
IF 3
Frontiers in Artificial Intelligence Pub Date : 2024-08-01 eCollection Date: 2024-01-01 DOI: 10.3389/frai.2024.1369702
Jakob Sommer, Fiona Dierksen, Tal Zeevi, Anh Tuan Tran, Emily W Avery, Adrian Mak, Ajay Malhotra, Charles C Matouk, Guido J Falcone, Victor Torres-Lopez, Sanjey Aneja, James Duncan, Lauren H Sansing, Kevin N Sheth, Seyedmehdi Payabvash
{"title":"Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.","authors":"Jakob Sommer, Fiona Dierksen, Tal Zeevi, Anh Tuan Tran, Emily W Avery, Adrian Mak, Ajay Malhotra, Charles C Matouk, Guido J Falcone, Victor Torres-Lopez, Sanjey Aneja, James Duncan, Lauren H Sansing, Kevin N Sheth, Seyedmehdi Payabvash","doi":"10.3389/frai.2024.1369702","DOIUrl":"10.3389/frai.2024.1369702","url":null,"abstract":"<p><strong>Purpose: </strong>Computed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after anterior circulation LVO thrombectomy based on admission CTAs.</p><p><strong>Methods: </strong>We split a dataset of 591 patients into training/cross-validation (n = 496) and independent test set (n = 95). We trained separate models for outcome prediction based on admission \"CTA\" images alone, \"CTA + Treatment\" (including time to thrombectomy and reperfusion success information), and \"CTA + Treatment  + Clinical\" (including admission age, sex, and NIH stroke scale). A binary (favorable) outcome was defined based on a 3-month modified Rankin Scale ≤ 2. The model was trained on our dataset based on the pre-trained ResNet-50 3D Convolutional Neural Network (\"MedicalNet\") and included CTA preprocessing steps.</p><p><strong>Results: </strong>We generated an ensemble model from the 5-fold cross-validation, and tested it in the independent test cohort, with receiver operating characteristic area under the curve (AUC, 95% confidence interval) of 70 (0.59-0.81) for \"CTA,\" 0.79 (0.70-0.89) for \"CTA + Treatment,\" and 0.86 (0.79-0.94) for \"CTA + Treatment + Clinical\" input models. A \"Treatment + Clinical\" logistic regression model achieved an AUC of 0.86 (0.79-0.93).</p><p><strong>Conclusion: </strong>Our results show the feasibility of an end-to-end automated model to predict outcomes from admission and post-thrombectomy reperfusion success. Such a model can facilitate prognostication in telehealth transfer and when a thorough neurological exam is not feasible due to language barrier or pre-existing morbidities.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1369702"},"PeriodicalIF":3.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989085","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}
引用次数: 0
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