Pub Date : 2024-12-01DOI: 10.1016/j.nuclcard.2024.102092
Marcelo F. Di Carli MD, MASNC
{"title":"A year of milestones and progress: Reflecting on 2024 in nuclear cardiology","authors":"Marcelo F. Di Carli MD, MASNC","doi":"10.1016/j.nuclcard.2024.102092","DOIUrl":"10.1016/j.nuclcard.2024.102092","url":null,"abstract":"","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":"42 ","pages":"Article 102092"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864546","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}
Pub Date : 2024-12-01DOI: 10.1016/j.nuclcard.2024.102087
Olivier F. Clerc MD, MPH , Antti Saraste MD, PhD
{"title":"The heart has a memory: Let's not forget to interrogate it with 18F-FDG PET","authors":"Olivier F. Clerc MD, MPH , Antti Saraste MD, PhD","doi":"10.1016/j.nuclcard.2024.102087","DOIUrl":"10.1016/j.nuclcard.2024.102087","url":null,"abstract":"","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":"42 ","pages":"Article 102087"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864552","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}
Pub Date : 2024-12-01DOI: 10.1016/j.nuclcard.2024.102012
Yousif A. Lucinian , Patrick Martineau , Gad Abikhzer , Francois Harel , Matthieu Pelletier-Galarneau MD, MSC
Myocardial inflammation plays a central role in the pathophysiology of various cardiac diseases. While FDG-PET is currently the primary method for molecular imaging of myocardial inflammation, its effectiveness is hindered by physiological myocardial uptake as well as its propensity for uptake by multiple disease-specific mechanisms. Novel radiotracers targeting diverse inflammatory immune cells and molecular pathways may provide unique insight through the visualization of underlying mechanisms central to the pathogenesis of inflammatory cardiac diseases, offering opportunities for increased understanding of immunocardiology. Moreover, the potentially enhanced specificity may lead to better quantification of disease activity, aiding in the guidance and monitoring of immunomodulatory therapy. This review aims to provide an update on advancements in non-FDG radiotracers for imaging myocardial inflammatory diseases, with a focus on cardiac sarcoidosis, myocarditis, and acute myocardial infarction.
{"title":"Novel tracers to assess myocardial inflammation with radionuclide imaging","authors":"Yousif A. Lucinian , Patrick Martineau , Gad Abikhzer , Francois Harel , Matthieu Pelletier-Galarneau MD, MSC","doi":"10.1016/j.nuclcard.2024.102012","DOIUrl":"10.1016/j.nuclcard.2024.102012","url":null,"abstract":"<div><div>Myocardial inflammation plays a central role in the pathophysiology of various cardiac diseases. While FDG-PET is currently the primary method for molecular imaging of myocardial inflammation, its effectiveness is hindered by physiological myocardial uptake as well as its propensity for uptake by multiple disease-specific mechanisms. Novel radiotracers targeting diverse inflammatory immune cells and molecular pathways may provide unique insight through the visualization of underlying mechanisms central to the pathogenesis of inflammatory cardiac diseases, offering opportunities for increased understanding of immunocardiology. Moreover, the potentially enhanced specificity may lead to better quantification of disease activity, aiding in the guidance and monitoring of immunomodulatory therapy. This review aims to provide an update on advancements in non-FDG radiotracers for imaging myocardial inflammatory diseases, with a focus on cardiac sarcoidosis, myocarditis, and acute myocardial infarction.</div></div>","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":"42 ","pages":"Article 102012"},"PeriodicalIF":3.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1016/j.nuclcard.2024.102089
Valerie Builoff, Aakash Shanbhag, Robert Jh Miller, Damini Dey, Joanna X Liang, Kathleen Flood, Jamieson M Bourque, Panithaya Chareonthaitawee, Lawrence M Phillips, Piotr J Slomka
Background: Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. This study assesses four LLMs-GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.)-in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination.
Methods: We used 168 questions: 141 text-only and 27 image-based, categorized into four sections mirroring the CBNC exam. Each LLM was presented with the same standardized prompt and applied to each section 30 times to account for stochasticity. Performance over six weeks was assessed for all models except GPT-4o. McNemar's test compared correct response proportions.
Results: GPT-4, Gemini, GPT-4 Turbo, and GPT-4o correctly answered median percentages of 56.8% (95% confidence interval 55.4% - 58.0%), 40.5% (39.9% - 42.9%), 60.7% (59.5% - 61.3%), and 63.1% (62.5%-64.3%) of questions, respectively. GPT-4o significantly outperformed other models (P = .007 vs GPT-4 Turbo, P < .001 vs GPT-4 and Gemini). GPT-4o excelled on text-only questions compared to GPT-4, Gemini, and GPT-4 Turbo (P < .001, P < .001, and P = .001), while Gemini performed worse on image-based questions (P < .001 for all).
Conclusion: GPT-4o demonstrated superior performance among the four LLMs, achieving scores likely within or just outside the range required to pass a test akin to the CBNC examination. Although improvements in medical image interpretation are needed, GPT-4o shows potential to support physicians in answering text-based clinical questions.
{"title":"Evaluating AI proficiency in nuclear cardiology: Large language models take on the board preparation exam.","authors":"Valerie Builoff, Aakash Shanbhag, Robert Jh Miller, Damini Dey, Joanna X Liang, Kathleen Flood, Jamieson M Bourque, Panithaya Chareonthaitawee, Lawrence M Phillips, Piotr J Slomka","doi":"10.1016/j.nuclcard.2024.102089","DOIUrl":"10.1016/j.nuclcard.2024.102089","url":null,"abstract":"<p><strong>Background: </strong>Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. This study assesses four LLMs-GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.)-in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination.</p><p><strong>Methods: </strong>We used 168 questions: 141 text-only and 27 image-based, categorized into four sections mirroring the CBNC exam. Each LLM was presented with the same standardized prompt and applied to each section 30 times to account for stochasticity. Performance over six weeks was assessed for all models except GPT-4o. McNemar's test compared correct response proportions.</p><p><strong>Results: </strong>GPT-4, Gemini, GPT-4 Turbo, and GPT-4o correctly answered median percentages of 56.8% (95% confidence interval 55.4% - 58.0%), 40.5% (39.9% - 42.9%), 60.7% (59.5% - 61.3%), and 63.1% (62.5%-64.3%) of questions, respectively. GPT-4o significantly outperformed other models (P = .007 vs GPT-4 Turbo, P < .001 vs GPT-4 and Gemini). GPT-4o excelled on text-only questions compared to GPT-4, Gemini, and GPT-4 Turbo (P < .001, P < .001, and P = .001), while Gemini performed worse on image-based questions (P < .001 for all).</p><p><strong>Conclusion: </strong>GPT-4o demonstrated superior performance among the four LLMs, achieving scores likely within or just outside the range required to pass a test akin to the CBNC examination. Although improvements in medical image interpretation are needed, GPT-4o shows potential to support physicians in answering text-based clinical questions.</p>","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":" ","pages":"102089"},"PeriodicalIF":3.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769756","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}
Pub Date : 2024-11-17DOI: 10.1016/j.nuclcard.2024.102086
Rabih Touma, Mario Njeim, Victor Jebara, Aiden Abidov
{"title":"Cardiac PET and CMR in diagnosis of epi-pericardial fat necrosis associated with severe refractory chest pain following arrhythmia ablation.","authors":"Rabih Touma, Mario Njeim, Victor Jebara, Aiden Abidov","doi":"10.1016/j.nuclcard.2024.102086","DOIUrl":"10.1016/j.nuclcard.2024.102086","url":null,"abstract":"","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":" ","pages":"102086"},"PeriodicalIF":3.0,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648340","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}
Pub Date : 2024-11-14DOI: 10.1016/j.nuclcard.2024.102079
Santiago Callegari, Carlos Mena-Hurtado, Kim G Smolderen, Stephanie Thorn, Albert J Sinusas
Lower extremity peripheral artery disease (PAD) is characterized by impairment of blood flow associated with arterial stenosis and frequently coexisting microvascular disease and is associated with high rates of morbidity and mortality. Current diagnostic modalities have limited accuracy in early diagnosis, risk stratification, preprocedural assessment, and evaluation of therapy and are focused on the detection of obstructive atherosclerotic disease. Early diagnosis and assessment of both large vessels and microcirculation may improve risk stratification and guide therapeutic interventions. Single-photon emission computed tomography and positron emission tomography imaging have been shown to be accurate to detect changes in perfusion in preclinical models and clinical disease, and have the potential to overcome limitations of existing diagnostic modalities, while offering novel information about perfusion, metabolic, and molecular processes. This review provides a comprehensive reassessment of radiotracer-based imaging of PAD in preclinical and clinical studies, emphasizing the challenges that arise due to the complex physiology in the peripheral vasculature. We will also highlight the latest advancements, underscoring emerging artificial intelligence and big data analysis, as well as clinically relevant areas where the field could advance in the next decade.
下肢外周动脉疾病（PAD）的特征是与动脉狭窄和经常并存的微血管疾病有关的血流障碍，发病率和死亡率都很高。目前的诊断方法在早期诊断、风险分层、手术前评估和治疗评估方面的准确性有限，而且主要集中在阻塞性动脉粥样硬化疾病的检测上。对大血管和微循环的早期诊断和评估可改善风险分层并指导治疗干预。SPECT 和 PET 成像已被证明能准确检测临床前模型和临床疾病中的灌注变化，并有可能克服现有诊断模式的局限性，同时提供有关灌注、代谢和分子过程的新信息。本综述对临床前和临床研究中基于放射性示踪剂的 PAD 成像进行了全面的重新评估，强调了外周血管复杂的生理结构所带来的挑战。我们还将重点介绍最新进展，强调新兴的人工智能和大数据分析，以及该领域在未来十年可能取得进展的临床相关领域。
{"title":"New horizons in nuclear cardiology: Imaging of peripheral arterial disease.","authors":"Santiago Callegari, Carlos Mena-Hurtado, Kim G Smolderen, Stephanie Thorn, Albert J Sinusas","doi":"10.1016/j.nuclcard.2024.102079","DOIUrl":"10.1016/j.nuclcard.2024.102079","url":null,"abstract":"<p><p>Lower extremity peripheral artery disease (PAD) is characterized by impairment of blood flow associated with arterial stenosis and frequently coexisting microvascular disease and is associated with high rates of morbidity and mortality. Current diagnostic modalities have limited accuracy in early diagnosis, risk stratification, preprocedural assessment, and evaluation of therapy and are focused on the detection of obstructive atherosclerotic disease. Early diagnosis and assessment of both large vessels and microcirculation may improve risk stratification and guide therapeutic interventions. Single-photon emission computed tomography and positron emission tomography imaging have been shown to be accurate to detect changes in perfusion in preclinical models and clinical disease, and have the potential to overcome limitations of existing diagnostic modalities, while offering novel information about perfusion, metabolic, and molecular processes. This review provides a comprehensive reassessment of radiotracer-based imaging of PAD in preclinical and clinical studies, emphasizing the challenges that arise due to the complex physiology in the peripheral vasculature. We will also highlight the latest advancements, underscoring emerging artificial intelligence and big data analysis, as well as clinically relevant areas where the field could advance in the next decade.</p>","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":" ","pages":"102079"},"PeriodicalIF":3.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644447","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}
Pub Date : 2024-11-07DOI: 10.1016/j.nuclcard.2024.102070
Banu SathyaMurthi, Sruthi Sarish, Muhammad Arsalan Khan, Parthiban Arumugam
{"title":"Benefit of 82-Rubidium Positron Emission Tomography for risk stratification in a patient with Intrapericardial diaphragmatic hernia.","authors":"Banu SathyaMurthi, Sruthi Sarish, Muhammad Arsalan Khan, Parthiban Arumugam","doi":"10.1016/j.nuclcard.2024.102070","DOIUrl":"10.1016/j.nuclcard.2024.102070","url":null,"abstract":"","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":" ","pages":"102070"},"PeriodicalIF":3.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142622255","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}
Pub Date : 2024-11-01DOI: 10.1016/j.nuclcard.2024.102055
Venkatesh L. Murthy MD, PhD
{"title":"Dawn of the cardiac PET era","authors":"Venkatesh L. Murthy MD, PhD","doi":"10.1016/j.nuclcard.2024.102055","DOIUrl":"10.1016/j.nuclcard.2024.102055","url":null,"abstract":"","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":"41 ","pages":"Article 102055"},"PeriodicalIF":3.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142813351","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}
Pub Date : 2024-11-01DOI: 10.1016/j.nuclcard.2024.102080
Marcelo F. Di Carli MD, MASNC
{"title":"Quantifying progress: Myocardial blood flow measurements as catalyst for enhanced CAD management","authors":"Marcelo F. Di Carli MD, MASNC","doi":"10.1016/j.nuclcard.2024.102080","DOIUrl":"10.1016/j.nuclcard.2024.102080","url":null,"abstract":"","PeriodicalId":16476,"journal":{"name":"Journal of Nuclear Cardiology","volume":"41 ","pages":"Article 102080"},"PeriodicalIF":3.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142813352","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号