Diagnostic and Interventional Imaging最新文献

{"title":"Dural arteriovenous fistula masquerading as neuromyelitis optica spectrum disorder","authors":"Sylvain Bourdoncle , Caroline Papeix , Augustin Lecler","doi":"10.1016/j.diii.2024.09.003","DOIUrl":"10.1016/j.diii.2024.09.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 43-44"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence solutions for head and neck CT angiography: Ready for prime time?","authors":"Alexandre Bani-Sadr , Augustin Lecler","doi":"10.1016/j.diii.2024.09.005","DOIUrl":"10.1016/j.diii.2024.09.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 1-2"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in interventional radiology: Current concepts and future trends","authors":"Armelle Lesaunier ,&nbsp;Julien Khlaut ,&nbsp;Corentin Dancette ,&nbsp;Lambros Tselikas ,&nbsp;Baptiste Bonnet ,&nbsp;Tom Boeken","doi":"10.1016/j.diii.2024.08.004","DOIUrl":"10.1016/j.diii.2024.08.004","url":null,"abstract":"<div><div>While artificial intelligence (AI) is already well established in diagnostic radiology, it is beginning to make its mark in interventional radiology. AI has the potential to dramatically change the daily practice of interventional radiology at several levels. In the preoperative setting, recent advances in deep learning models, particularly foundation models, enable effective management of multimodality and increased autonomy through their ability to function minimally without supervision. Multimodality is at the heart of patient-tailored management and in interventional radiology, this translates into the development of innovative models for patient selection and outcome prediction. In the perioperative setting, AI is manifesting itself in applications that assist radiologists in image analysis and real-time decision making, thereby improving the efficiency, accuracy, and safety of interventions. In synergy with advances in robotic technologies, AI is laying the groundwork for an increased autonomy. From a research perspective, the development of artificial health data, such as AI-based data augmentation, offers an innovative solution to this central issue and promises to stimulate research in this area. This review aims to provide the medical community with the most important current and future applications of AI in interventional radiology.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 5-10"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for bone fracture detection: A promising tool but no substitute for human expertise","authors":"Daphné Guenoun ,&nbsp;Mickaël Tordjman","doi":"10.1016/j.diii.2024.10.004","DOIUrl":"10.1016/j.diii.2024.10.004","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 3-4"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Node-RADS: Finally, something new on the front of cross-sectional imaging of lymph nodes?","authors":"Olivier Rouvière ,&nbsp;Laurence Rocher","doi":"10.1016/j.diii.2024.12.002","DOIUrl":"10.1016/j.diii.2024.12.002","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 4","pages":"Pages 109-110"},"PeriodicalIF":4.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in emergency neuroradiology: Current applications and perspectives","authors":"Bo Gong ,&nbsp;Farzad Khalvati ,&nbsp;Birgit B. Ertl-Wagner ,&nbsp;Michael N. Patlas","doi":"10.1016/j.diii.2024.11.002","DOIUrl":"10.1016/j.diii.2024.11.002","url":null,"abstract":"<div><div>Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brain, head and neck, and spine. This narrative review aims at providing an up-to-date discussion about the state of the art of applications of artificial intelligence in emergency neuroradiology, which have substantially expanded in depth and scope in the past few years. A detailed analysis of machine learning and deep learning algorithms in several tasks related to acute ischemic stroke involving various imaging modalities, including a description of existing commercial products, is provided. The applications of artificial intelligence in acute intracranial hemorrhage and other vascular pathologies such as intracranial aneurysm and arteriovenous malformation are discussed. Other areas of emergency neuroradiology including infection, fracture, cord compression, and pediatric imaging are further discussed in turn. Based on these discussions, this article offers insight into practical considerations regarding the applications of artificial intelligence in emergency neuroradiology, calling for more development driven by clinical needs, attention to pediatric neuroimaging, and analysis of real-world performance.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 4","pages":"Pages 135-142"},"PeriodicalIF":4.9,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in radiotherapy: Current applications and future trends","authors":"Paul Giraud ,&nbsp;Jean-Emmanuel Bibault","doi":"10.1016/j.diii.2024.06.001","DOIUrl":"10.1016/j.diii.2024.06.001","url":null,"abstract":"<div><div>Radiation therapy has dramatically changed with the advent of computed tomography and intensity modulation. This added complexity to the workflow but allowed for more precise and reproducible treatment. As a result, these advances required the accurate delineation of many more volumes, raising questions about how to delineate them, in a uniform manner across centers. Then, as computing power improved, reverse planning became possible and three-dimensional dose distributions could be generated. Artificial intelligence offers the opportunity to make such workflow more efficient while increasing practice homogeneity. Many artificial intelligence-based tools are being implemented in routine practice to increase efficiency, reduce workload and improve homogeneity of treatments. Data retrieved from this workflow could be combined with clinical data and omic data to develop predictive tools to support clinical decision-making process. Such predictive tools are at the stage of proof-of-concept and need to be explainatory, prospectively validated, and based on large and multicenter cohorts. Nevertheless, they could bridge the gap to personalized radiation oncology, by personalizing oncologic strategies, dose prescriptions to tumor volumes and dose constraints to organs at risk.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 12","pages":"Pages 475-480"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141451976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance and relationships of structural parameters and strain components for the diagnosis of cardiac amyloidosis with MRI","authors":"Youssef Zaarour ,&nbsp;Islem Sifaoui ,&nbsp;Haifa Remili ,&nbsp;Mounira Kharoubi ,&nbsp;Amira Zaroui ,&nbsp;Thibaud Damy ,&nbsp;Jean-François Deux","doi":"10.1016/j.diii.2024.08.002","DOIUrl":"10.1016/j.diii.2024.08.002","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to evaluate the diagnostic performance and relationships of cardiac MRI structural parameters and strain components in patients with cardiac amyloidosis (CA) and to estimate the capabilities of these variables to discriminate between CA and non-amyloid cardiac hypertrophy (NACH).</div></div><div><h3>Materials and methods</h3><div>Seventy patients with CA (56 men; mean age, 76 ± 10 [standard deviation] years) and 32 patients (19 men; mean age, 63 ± 10 [standard deviation] years) with NACH underwent cardiac MRI. Feature tracking (FT) global longitudinal strain (GLS), radial strain (GRS), circumferential strain (GCS), strain AB ratio (apical strain divided by basal strain), myocardial T1, myocardial T2 and extracellular volume (ECV) were calculated. Comparisons between patients with CA and those with NACH were made using Mann-Whitney rank sum test. The ability of each variable to discriminate between CA and NACH was estimated using area under the receiver operating characteristic curve (AUC).</div></div><div><h3>Results</h3><div>Patients with CA had higher median GLS (-7.0% [Q1, -9.0; Q3, -5.0]), higher median GCS (-12.0% [Q1, -15.0; Q3, -9.0]), and lower median GRS (16.5% [Q1, 13.0; Q3, 23.0]) than those with NACH (-9.0% [Q1, -11.0; Q3, -8.0]; -17.0% [Q1, -20.0; Q3, -14.0]; and 25.5% [Q1, 16.0; Q3, 31.5], respectively) (<em>P</em> &lt; 0.001 for all). Median myocardial T1 and ECV were significantly higher in patients with CA (1112 ms [Q1, 1074; Q3, 1146] and 47% [Q1, 41; Q3, 55], respectively) than in those with NACH (1056 ms [Q1, 1011; Q3, 1071] and 28% [Q1, 26; Q3, 30], respectively) (<em>P</em> &lt; 0.001). Basal ECV showed the best performance for the diagnosis of CA (AUC = 0.975; 95% confidence interval [CI]: 0.947–1). No differences in AUC were found between AB ratio of GRS (0.843; 95% CI: 0.768–0.918) and basal myocardial T1 (0.834; 95% CI: 0.741–0.928) for the diagnosis of CA (<em>P</em> = 0.81). The combination of the AB ratio of FT-GRS and basal myocardial T1 had a diagnostic performance not different from that of basal ECV (<em>P</em> = 0.06).</div></div><div><h3>Conclusion</h3><div>ECV outperforms FT-strain for the diagnosis of CA with cardiac MRI. The AB ratio of FT-GRS associated with myocardial T1 provides diagnostic performance similar to that achieved by ECV.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 12","pages":"Pages 489-497"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Myocardial strain imaging: Advancing the diagnosis of cardiac amyloidosis with MRI","authors":"Patrick Krumm","doi":"10.1016/j.diii.2024.09.007","DOIUrl":"10.1016/j.diii.2024.09.007","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 12","pages":"Pages 471-472"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving 4D flow cardiac MRI analysis to accurately assess aortic regurgitation in patients with bicuspid aortic valves","authors":"Arshid Azarine ,&nbsp;Jean-Nicolas Dacher","doi":"10.1016/j.diii.2024.10.006","DOIUrl":"10.1016/j.diii.2024.10.006","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"105 12","pages":"Pages 473-474"},"PeriodicalIF":4.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142630735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}