Pub Date : 2026-01-01Epub Date: 2025-10-10DOI: 10.1016/j.jcmg.2025.08.019
Xuan Ma MD , Yun Tang MD , Xingrui Chen MD , Shujuan Yang MD , Jiaxin Wang MD , Kai Yang MD , Zhixiang Dong MD , Zhuxin Wei MD , Xi Jia MD , Yujie Liu MD , Pengyu Zhou MD , Kankan Zhao PhD , Yanyan Song MD , Minjie Lu MD, PhD , Xiuyu Chen MD , Shihua Zhao MD, PhD
Background
Left ventricular (LV)-global longitudinal strain (GLS) assessed by cardiac magnetic resonance (CMR) feature tracking is an emerging marker for predicting adverse outcomes in hypertrophic cardiomyopathy (HCM), but its incremental prognostic value and mechanistic role in sudden cardiac death (SCD) risk stratification remain unclear.
Objectives
The study sought to evaluate whether LV-GLS adds prognostic value beyond current ESC (European Society of Cardiology) and ACC (American College of Cardiology)/AHA (American Heart Association) SCD risk models, and mediates the relationship between myocardial abnormalities and SCD risk in HCM.
Methods
The authors retrospectively analyzed 2,009 patients with HCM (mean age: 50 ± 14 years, 70% men) who underwent CMR between 2010 and 2017. LV-GLS was quantified using cine CMR feature tracking. The primary endpoint included SCD and aborted SCD. Prognostic performance was assessed using time-dependent receiver-operating characteristic analysis and competing risk regression. Mediation analysis was used to investigate how LV-GLS mediated associations between myocardial hypertrophy, fibrosis, and SCD.
Results
Over a median follow-up of 88.2 months, 85 (4.2%) patients experienced SCD events. These patients had significantly lower absolute LV-GLS values (9.0% ± 3.6% vs 11.1% ± 3.6%; P < 0.001). In competing-risk regression, LV-GLS independently predicted SCD after adjustment for ESC (subdistribution HR [sHR]: 1.12 per 1% decrease [95% CI: 1.06-1.22]; P < 0.001) and ACC/AHA risk factors (sHR: 1.09 [95% CI: 1.02-1.18]; P = 0.016). Adding LV-GLS improved the 5-year predictive accuracy of both ESC and ACC/AHA models (AUC from 0.72 to 0.77 and from 0.71 to 0.76, respectively). Absolute LV-GLS with a cutoff of 9.23% further stratified risk in patient subgroups with either class II or class III implantable cardioverter-defibrillator indications (all log-rank P < 0.001). Mediation analysis showed LV-GLS partially mediated the effect of maximum wall thickness and extent of fibrosis on SCD (proportion-mediated: 17.5% and 23.1%, respectively; both P < 0.001).
Conclusions
In patients with HCM, CMR-derived LV-GLS is an incremental predictor of SCD beyond current guideline-based risk models and partially mediates the association between myocardial abnormalities and SCD.
背景：通过心脏磁共振（CMR）特征跟踪评估左心室(LV)-全局纵向应变（GLS）是预测肥厚性心肌病（HCM）不良结局的新兴标志物，但其增量预后价值和在心源性猝死（SCD）风险分层中的机制作用尚不清楚。目的：本研究旨在评估LV-GLS是否在现有ESC（欧洲心脏病学会）和ACC（美国心脏病学会）/AHA（美国心脏协会）SCD风险模型之外增加了预后价值，并介导HCM中心肌异常和SCD风险之间的关系。方法回顾性分析2010年至2017年期间接受CMR治疗的2009例HCM患者（平均年龄：50±14岁，70%为男性）。采用电影CMR特征跟踪量化LV-GLS。主要终点包括SCD和流产的SCD。预后表现评估采用时间依赖的接受者-操作特征分析和竞争风险回归。采用中介分析探讨LV-GLS如何介导心肌肥大、纤维化和SCD之间的关联。结果中位随访88.2个月，85例（4.2%）患者发生SCD事件。这些患者的LV-GLS绝对值明显降低（9.0%±3.6% vs 11.1%±3.6%;P < 0.001）。在竞争风险回归中，LV-GLS在调整ESC（亚分布HR [sHR]: 1.12 / 1%降低[95% CI: 1.06-1.22]; P < 0.001）和ACC/AHA危险因素（sHR: 1.09 [95% CI: 1.02-1.18]; P = 0.016）后独立预测SCD。添加LV-GLS提高了ESC和ACC/AHA模型的5年预测精度（AUC分别从0.72到0.77和0.71到0.76）。在II类或III类植入式心律转复除颤器适应症的患者亚组中，绝对LV-GLS的截止值为9.23%，进一步分层风险（所有log-rank P < 0.001）。中介分析显示，LV-GLS部分介导最大壁厚和纤维化程度对SCD的影响（比例介导：分别为17.5%和23.1%，P均< 0.001）。结论：在HCM患者中，cmr衍生的LV-GLS是SCD的增量预测因子，超出了目前基于指南的风险模型，并部分介导心肌异常与SCD之间的关联。
{"title":"Feature Tracking–Derived Global Longitudinal Strain Enhances Risk Stratification for Sudden Cardiac Death in Hypertrophic Cardiomyopathy","authors":"Xuan Ma MD , Yun Tang MD , Xingrui Chen MD , Shujuan Yang MD , Jiaxin Wang MD , Kai Yang MD , Zhixiang Dong MD , Zhuxin Wei MD , Xi Jia MD , Yujie Liu MD , Pengyu Zhou MD , Kankan Zhao PhD , Yanyan Song MD , Minjie Lu MD, PhD , Xiuyu Chen MD , Shihua Zhao MD, PhD","doi":"10.1016/j.jcmg.2025.08.019","DOIUrl":"10.1016/j.jcmg.2025.08.019","url":null,"abstract":"<div><h3>Background</h3><div>Left ventricular (LV)-global longitudinal strain (GLS) assessed by cardiac magnetic resonance (CMR) feature tracking is an emerging marker for predicting adverse outcomes in hypertrophic cardiomyopathy (HCM), but its incremental prognostic value and mechanistic role in sudden cardiac death (SCD) risk stratification remain unclear.</div></div><div><h3>Objectives</h3><div>The study sought to evaluate whether LV-GLS adds prognostic value beyond current ESC (European Society of Cardiology) and ACC (American College of Cardiology)/AHA (American Heart Association) SCD risk models, and mediates the relationship between myocardial abnormalities and SCD risk in HCM.</div></div><div><h3>Methods</h3><div>The authors retrospectively analyzed 2,009 patients with HCM (mean age: 50 ± 14 years, 70% men) who underwent CMR between 2010 and 2017. LV-GLS was quantified using cine CMR feature tracking. The primary endpoint included SCD and aborted SCD. Prognostic performance was assessed using time-dependent receiver-operating characteristic analysis and competing risk regression. Mediation analysis was used to investigate how LV-GLS mediated associations between myocardial hypertrophy, fibrosis, and SCD.</div></div><div><h3>Results</h3><div>Over a median follow-up of 88.2 months, 85 (4.2%) patients experienced SCD events. These patients had significantly lower absolute LV-GLS values (9.0% ± 3.6% vs 11.1% ± 3.6%; <em>P</em> < 0.001). In competing-risk regression, LV-GLS independently predicted SCD after adjustment for ESC (subdistribution HR [sHR]: 1.12 per 1% decrease [95% CI: 1.06-1.22]; <em>P <</em> 0.001) and ACC/AHA risk factors (sHR: 1.09 [95% CI: 1.02-1.18]; <em>P =</em> 0.016). Adding LV-GLS improved the 5-year predictive accuracy of both ESC and ACC/AHA models (AUC from 0.72 to 0.77 and from 0.71 to 0.76, respectively). Absolute LV-GLS with a cutoff of 9.23% further stratified risk in patient subgroups with either class II or class III implantable cardioverter-defibrillator indications (all log-rank <em>P <</em> 0.001). Mediation analysis showed LV-GLS partially mediated the effect of maximum wall thickness and extent of fibrosis on SCD (proportion-mediated: 17.5% and 23.1%, respectively; both <em>P <</em> 0.001).</div></div><div><h3>Conclusions</h3><div>In patients with HCM, CMR-derived LV-GLS is an incremental predictor of SCD beyond current guideline-based risk models and partially mediates the association between myocardial abnormalities and SCD.</div></div>","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 33-45"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-10-09DOI: 10.1016/j.jcmg.2025.07.022
Arthur Shiyovich MD , Avinainder Singh MBBS , Camila V. Blair MD , Rhanderson Cardoso MD , Daniel Huck MD, MPH , Gary Peng MD, PhD , Leslee J. Shaw PhD , Jonathon A. Leipsic MD , Christoph Gräni MD , Charalambos Antoniades MD, PhD , Pál Maurovich-Horvat MD, PhD, MPH , Eric E. Williamson MD , Filippo Cademartiri MD, PhD , Stephan Achenbach MD , Ron Blankstein MD
Coronary computed tomography angiography plays a pivotal role in the diagnosis, risk stratification, and treatment of patients with known or suspected coronary artery disease. However, conventional computed tomography (CT) technologies are limited by spatial resolution, artifact susceptibility, and radiation exposure. Photon-counting computed tomography (PCCT) introduces substantial technological improvements over conventional CT. This includes improved spatial and contrast resolution, energy discrimination, and reduction of various artifacts. As a result, PCCT enables superior coronary lumen and plaque evaluation, even in complex cases with severe calcification or smaller coronary stents. Beyond the coronary arteries, PCCT offers improved visualization of cardiac anatomy and myocardial tissue characterization with the potential to reduce downstream testing, improve diagnosis and treatment, and ultimately improve clinical outcomes. PCCT is poised to become the dominant technology for cardiovascular CT; however, challenges such as high costs, increased data demands, and a need for more validation, standardized image acquisition, and post-processing protocols remain. This review explores the technical principles of PCCT, its advantages over conventional CT, and its current and potential future applications in cardiac imaging, highlighting opportunities for future research.
{"title":"Photon-Counting Computed Tomography in Cardiac Imaging","authors":"Arthur Shiyovich MD , Avinainder Singh MBBS , Camila V. Blair MD , Rhanderson Cardoso MD , Daniel Huck MD, MPH , Gary Peng MD, PhD , Leslee J. Shaw PhD , Jonathon A. Leipsic MD , Christoph Gräni MD , Charalambos Antoniades MD, PhD , Pál Maurovich-Horvat MD, PhD, MPH , Eric E. Williamson MD , Filippo Cademartiri MD, PhD , Stephan Achenbach MD , Ron Blankstein MD","doi":"10.1016/j.jcmg.2025.07.022","DOIUrl":"10.1016/j.jcmg.2025.07.022","url":null,"abstract":"<div><div>Coronary computed tomography angiography plays a pivotal role in the diagnosis, risk stratification, and treatment of patients with known or suspected coronary artery disease. However, conventional computed tomography (CT) technologies are limited by spatial resolution, artifact susceptibility, and radiation exposure. Photon-counting computed tomography (PCCT) introduces substantial technological improvements over conventional CT. This includes improved spatial and contrast resolution, energy discrimination, and reduction of various artifacts. As a result, PCCT enables superior coronary lumen and plaque evaluation, even in complex cases with severe calcification or smaller coronary stents. Beyond the coronary arteries, PCCT offers improved visualization of cardiac anatomy and myocardial tissue characterization with the potential to reduce downstream testing, improve diagnosis and treatment, and ultimately improve clinical outcomes. PCCT is poised to become the dominant technology for cardiovascular CT; however, challenges such as high costs, increased data demands, and a need for more validation, standardized image acquisition, and post-processing protocols remain. This review explores the technical principles of PCCT, its advantages over conventional CT, and its current and potential future applications in cardiac imaging, highlighting opportunities for future research.</div></div>","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 94-117"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-08-22DOI: 10.1016/j.jcmg.2025.07.008
Niklas Beyhoff MD, Owen P. Agnel MSc, Maximilian Fenski MD, Christina Botrous MBBS, MSc, Yi Jie Gifford Tan BA, BM BCh, Robert W. Smillie BA, BM BCh, Zakariye Ashkir MD, Lucy E.M. Finnigan PhD, Sarahfaye F. Dolman MPH, Paul Kolm PhD, William S. Weintraub MD, Raymond Y. Kwong MD, MPH, Michael Jerosch-Herold PhD, Milind Y. Desai MD, Carolyn Y. Ho MD, Patrice Desvigne-Nickens MD, John P. DiMarco MD, Barbara Casadei MD, DPhil, Hugh C. Watkins MD, PhD, Christopher M. Kramer MD, Betty Raman MBBS, DPhil
{"title":"Left Atrial Reservoir Strain Predicts Atrial Fibrillation in Hypertrophic Cardiomyopathy","authors":"Niklas Beyhoff MD, Owen P. Agnel MSc, Maximilian Fenski MD, Christina Botrous MBBS, MSc, Yi Jie Gifford Tan BA, BM BCh, Robert W. Smillie BA, BM BCh, Zakariye Ashkir MD, Lucy E.M. Finnigan PhD, Sarahfaye F. Dolman MPH, Paul Kolm PhD, William S. Weintraub MD, Raymond Y. Kwong MD, MPH, Michael Jerosch-Herold PhD, Milind Y. Desai MD, Carolyn Y. Ho MD, Patrice Desvigne-Nickens MD, John P. DiMarco MD, Barbara Casadei MD, DPhil, Hugh C. Watkins MD, PhD, Christopher M. Kramer MD, Betty Raman MBBS, DPhil","doi":"10.1016/j.jcmg.2025.07.008","DOIUrl":"10.1016/j.jcmg.2025.07.008","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 133-135"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-08DOI: 10.1016/j.jcmg.2025.09.001
Leslee J. Shaw PhD , Ron Blankstein MD , Jonathon A. Leipsic MD , Partho P. Sengupta MD , Koen Nieman MD , Jeroen J. Bax MD, PhD , William A. Zoghbi MD , Y. Chandrashekhar MD
{"title":"Clinical Integration of AI-Enabled Plaque Quantification to Improve Cardiovascular Risk Stratification","authors":"Leslee J. Shaw PhD , Ron Blankstein MD , Jonathon A. Leipsic MD , Partho P. Sengupta MD , Koen Nieman MD , Jeroen J. Bax MD, PhD , William A. Zoghbi MD , Y. Chandrashekhar MD","doi":"10.1016/j.jcmg.2025.09.001","DOIUrl":"10.1016/j.jcmg.2025.09.001","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 61-63"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145032152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-30DOI: 10.1016/j.jcmg.2025.08.011
Mostafa A. Al-Alusi MD, MS , Emily S. Lau MD, MPH , Aeron M. Small MD, MTR , Christopher Reeder PhD , Tal Shnitzer PhD , Carl T. Andrews MS , Shinwan Kany MD , Joel T. Rämö MD , Julian S. Haimovich MD , Shaan Khurshid MD, MPH , Danita Y. Sanborn MD, MMSc , Michael H. Picard MD , Jennifer E. Ho MD , Mahnaz Maddah PhD , Patrick T. Ellinor MD, PhD
Background
Mitral valve prolapse (MVP) has a prevalence of 2% to 3% and increases risk of heart failure and sudden death, but diagnosis by transthoracic echocardiography requires time and expertise.
Objectives
This study aims to develop a deep learning model DROID-MVP (Dimensional Reconstruction of Imaging Data–Mitral Valve Prolapse) to classify MVP from digital echocardiogram videos.
Methods
DROID-MVP was trained and validated using 1,043,893 echocardiogram videos (48,829 studies) from 16,902 cardiology patients at MGH (Massachusetts General Hospital), and externally validated in 8,888 MGH primary care patients and 257 primary care patients at BWH (Brigham and Women’s Hospital). The authors tested associations among DROID-MVP predictions (range: 0-1), mitral regurgitation (MR) severity, and mitral valve repair or replacement (MVR).
Results
Of 16,902 patients (6,391 [38%] women; age 61 ± 16 years) in the derivation sample, 783 (4.6%) had MVP. DROID-MVP accurately identified MVP across the MGH cardiology internal validation set (area under the receiver-operating characteristic curve [AUROC]: 0.947 [95% CI: 0.910-0.984]; average precision [AP]: 0.682 [95% CI: 0.565-0.784]; prevalence: 0.036), MGH primary care external validation set (AUROC: 0.964 [95% CI: 0.951-0.977]; AP: 0.651 [95% CI: 0.578-0.716]; prevalence: 0.022), and BWH primary care external validation set (AUROC: 0.968 [95% CI: 0.946-0.989]; AP: 0.774 [95% CI: 0.666-0.797]; prevalence: 0.113). A high (>0.67) vs low (<0.33) DROID-MVP score was associated with moderate or severe MR (adjusted OR: 2.0 [95% CI: 1.1-3.8]; P = 0.030) and future MVR (adjusted HR: 3.7 [95% CI: 1.5-8.9]; P = 0.004).
Conclusions
A deep learning model identifies MVP from echocardiogram videos, and model predictions are associated with clinical endpoints including MR and future MVR. Deep learning can automate MVP diagnosis and potentially generate digital markers of clinically significant MVP.
背景：二尖瓣脱垂（MVP）的患病率为2%至3%，并增加心力衰竭和猝死的风险，但经胸超声心动图诊断需要时间和专业知识。目的建立深度学习模型DROID-MVP （Dimensional Reconstruction of Imaging data -二尖瓣脱垂），对数字超声心动图视频中的二尖瓣脱垂进行分类。方法sdroid - mvp通过来自麻省总医院（MGH） 16902名心脏病患者的1,043,893个超声心动图视频（48,829项研究）进行培训和验证，并在8888名MGH初级保健患者和257名BWH （Brigham and Women Hospital）初级保健患者中进行外部验证。作者测试了DROID-MVP预测（范围：0-1）、二尖瓣反流（MR）严重程度和二尖瓣修复或置换（MVR）之间的关系。结果在衍生样本的16902例患者中（6391例（38%）女性，年龄61±16岁），783例（4.6%）有MVP。DROID-MVP准确识别了MGH心内科内部验证集（患者工作特征曲线下面积[AUROC]: 0.947 [95% CI: 0.910-0.984]，平均精度[AP]: 0.682 [95% CI: 0.565-0.784]，患病率：0.036），MGH初级保健外部验证集（AUROC: 0.964 [95% CI: 0.951-0.977], AP: 0.651 [95% CI: 0.578-0.716]，患病率：0.022），BWH初级保健外部验证集(AUROC: 0.968 [95% CI: 0.946-0.989], AP: 0.774 [95% CI: 0.666-0.797]；流行:0.113)。高（>0.67）vs低（<0.33）DROID-MVP评分与中度或重度MR（校正or: 2.0 [95% CI: 1.1-3.8]; P = 0.030）和未来MVR（校正HR: 3.7 [95% CI: 1.5-8.9]; P = 0.004）相关。结论深度学习模型从超声心动图视频中识别MVP，模型预测与临床终点相关，包括MR和未来MVR。深度学习可以自动诊断MVP，并可能生成具有临床意义的MVP的数字标记。
{"title":"A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram","authors":"Mostafa A. Al-Alusi MD, MS , Emily S. Lau MD, MPH , Aeron M. Small MD, MTR , Christopher Reeder PhD , Tal Shnitzer PhD , Carl T. Andrews MS , Shinwan Kany MD , Joel T. Rämö MD , Julian S. Haimovich MD , Shaan Khurshid MD, MPH , Danita Y. Sanborn MD, MMSc , Michael H. Picard MD , Jennifer E. Ho MD , Mahnaz Maddah PhD , Patrick T. Ellinor MD, PhD","doi":"10.1016/j.jcmg.2025.08.011","DOIUrl":"10.1016/j.jcmg.2025.08.011","url":null,"abstract":"<div><h3>Background</h3><div>Mitral valve prolapse (MVP) has a prevalence of 2% to 3% and increases risk of heart failure and sudden death, but diagnosis by transthoracic echocardiography requires time and expertise.</div></div><div><h3>Objectives</h3><div>This study aims to develop a deep learning model DROID-MVP (Dimensional Reconstruction of Imaging Data–Mitral Valve Prolapse) to classify MVP from digital echocardiogram videos.</div></div><div><h3>Methods</h3><div>DROID-MVP was trained and validated using 1,043,893 echocardiogram videos (48,829 studies) from 16,902 cardiology patients at MGH (Massachusetts General Hospital), and externally validated in 8,888 MGH primary care patients and 257 primary care patients at BWH (Brigham and Women’s Hospital). The authors tested associations among DROID-MVP predictions (range: 0-1), mitral regurgitation (MR) severity, and mitral valve repair or replacement (MVR).</div></div><div><h3>Results</h3><div>Of 16,902 patients (6,391 [38%] women; age 61 ± 16 years) in the derivation sample, 783 (4.6%) had MVP. DROID-MVP accurately identified MVP across the MGH cardiology internal validation set (area under the receiver-operating characteristic curve [AUROC]: 0.947 [95% CI: 0.910-0.984]; average precision [AP]: 0.682 [95% CI: 0.565-0.784]; prevalence: 0.036), MGH primary care external validation set (AUROC: 0.964 [95% CI: 0.951-0.977]; AP: 0.651 [95% CI: 0.578-0.716]; prevalence: 0.022), and BWH primary care external validation set (AUROC: 0.968 [95% CI: 0.946-0.989]; AP: 0.774 [95% CI: 0.666-0.797]; prevalence: 0.113). A high (>0.67) vs low (<0.33) DROID-MVP score was associated with moderate or severe MR (adjusted OR: 2.0 [95% CI: 1.1-3.8]; <em>P =</em> 0.030) and future MVR (adjusted HR: 3.7 [95% CI: 1.5-8.9]; <em>P =</em> 0.004).</div></div><div><h3>Conclusions</h3><div>A deep learning model identifies MVP from echocardiogram videos, and model predictions are associated with clinical endpoints including MR and future MVR. Deep learning can automate MVP diagnosis and potentially generate digital markers of clinically significant MVP.</div></div>","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 18-29"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-08DOI: 10.1016/j.jcmg.2025.07.020
Michael W. Lim MBBS, Rahul G. Muthalaly MBBS, MPH, Geoffrey R. Wong MBBS, PhD, Ahmed M. Al-Kaisey MBChB, PhD, Damini Dey PhD, Thomas H. Marwick MBBS, MPH, PhD, Peter M. Kistler MBBS, PhD, Jonathan M. Kalman MBBS, PhD, Nitesh Nerlekar MBBS, MPH, PhD
{"title":"Peripulmonary Vein Adipose Tissue Attenuation as a Novel Marker of Atrial Fibrillation Risk","authors":"Michael W. Lim MBBS, Rahul G. Muthalaly MBBS, MPH, Geoffrey R. Wong MBBS, PhD, Ahmed M. Al-Kaisey MBChB, PhD, Damini Dey PhD, Thomas H. Marwick MBBS, MPH, PhD, Peter M. Kistler MBBS, PhD, Jonathan M. Kalman MBBS, PhD, Nitesh Nerlekar MBBS, MPH, PhD","doi":"10.1016/j.jcmg.2025.07.020","DOIUrl":"10.1016/j.jcmg.2025.07.020","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 130-132"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-16DOI: 10.1016/j.jcmg.2025.08.008
Ali Ali MD, William R. Miranda MD, Christopher Francois MD, Sara ElZalabany MBBCh, Amr Moustafa MBBCh, Heidi M. Connolly MD, Alexander C. Egbe MD, MPH, MS
{"title":"Predictors and Prognostic Implications of Progressive Systemic Ventricular Dysfunction in Adults With Fontan Palliation","authors":"Ali Ali MD, William R. Miranda MD, Christopher Francois MD, Sara ElZalabany MBBCh, Amr Moustafa MBBCh, Heidi M. Connolly MD, Alexander C. Egbe MD, MPH, MS","doi":"10.1016/j.jcmg.2025.08.008","DOIUrl":"10.1016/j.jcmg.2025.08.008","url":null,"abstract":"","PeriodicalId":14767,"journal":{"name":"JACC. Cardiovascular imaging","volume":"19 1","pages":"Pages 139-141"},"PeriodicalIF":15.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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