White Richard D, Demirer Mutlu, Sebro Ronnie A, Cortopassi Isabel O, Stowell Justin T, McCann Matthew R, Barry Timothy, Appleton Christopher P, Helgeson Scott A, Erdal Barbaros S
Division of Augmented Intelligence in Imaging, Mayo Clinic Florida, Jacksonville, FL, USA.
Division of Cardiothoracic Imaging, Department of Radiology, Mayo Clinic Florida, Jacksonville, FL, USA.
Sci Rep. 2025 Oct 31;15(1):38181. doi: 10.1038/s41598-025-22026-x.
Isolated-Left Ventricular Diastolic Dysfunction [LVDD] ranges (and may progress) from preclinical asymptomatic, symptomatic-LVDD, to LVDD-predominate Heart Failure [HF] presentations; if recognized early, LVDD progression might be preventable. Current early-HF screening remains limited, providing opportunities for insights from a standard Chest X-Ray [CXR]. While CXR assessment for "pulmonary congestion" supports suspected-HF evaluation in evidence-based guidelines, the potential for systematic Pulmonary Venous Hypertension [PVH]-Staging to contribute to initial detection and scaling of LVDD is unclear. This study compared CXR-based PVH-Staging to Doppler Echocardiography [DEcho]-based LVDD-Grading in the absence of systolic dysfunction. Questions included: (1) With PVH-Staging performed by cardiothoracic radiologists, what intra-/inter-reader variabilities remain? (2) Does PVH-Staging track LVDD-Grading? and (3) Can AI-assisted PVH prediction of LVDD-Grade match human performance? CXR examinations of 1,682 (including 750 asymptomatic/healthy) subjects, without: (1) Anatomical/physiological confounders of DEcho or CXR examinations (≤ 24 h apart), and (2) AI model-training confounders, were independently assigned 1 of 11 (9 PVH-related) Pulmonary Vasculature Patterns [PVPs] by 4 cardiothoracic radiologists and repeated for reliability evaluation. Expert-consensus Human Ground Truth [HGT] PVH PVPs were correlated with LVDD Grades (0 to 3-4), as were PVH-Rank predictions by a transformer-based AI model ["PVPI"]. Despite experience-dependent intra-/inter-reader reliability in PVP assignment, there was significant (p < 0.001) overall consistency. With increasing HGT PVH Stage, a significant (p < 0.001) trend towards increasing LVDD Grade was found; while PVH-Staging achieved confidence backing Grade 0/No LVDD, confident LVDD Grade recognition was not achieved until Grades 3-4/Restrictive Filling. However, a significantly (p < 0.001) stronger incrementally positive trend in PVPI PVH-Ranking with LVDD-Grading was demonstrated. Although validated, PVH-Staging for LVDD-Grading is limited by reader variabilities. AI-assisted PVH-Ranking may facilitate earlier and widespread objective CXR screening for LVDD which is ubiquitous in HF.
孤立性左心室舒张功能障碍[LVDD]的范围（且可能进展）从临床前无症状、有症状的LVDD，到以LVDD为主的心力衰竭[HF]表现；如果早期识别，LVDD的进展可能是可预防的。目前早期心力衰竭筛查仍然有限，这为从标准胸部X线[CXR]中获取见解提供了机会。虽然基于CXR评估“肺充血”在循证指南中支持疑似心力衰竭的评估，但系统的肺静脉高压[PVH]分期对LVDD的初始检测和分级的潜在作用尚不清楚。本研究在无收缩功能障碍的情况下，将基于CXR的PVH分期与基于多普勒超声心动图[DEcho]的LVDD分级进行了比较。问题包括：（1）心胸放射科医生进行PVH分期时，阅片者内/阅片者间的变异性如何？（2）PVH分期是否与LVDD分级相关？以及（3）人工智能辅助的PVH对LVDD分级的预测能否与人类表现相匹配？对1682名受试者（包括750名无症状/健康者）进行CXR检查，这些受试者没有：（1）DEcho或CXR检查的解剖学/生理学混杂因素（间隔≤24小时），以及（2）人工智能模型训练混杂因素，由4名心胸放射科医生独立将其分配到11种（9种与PVH相关）肺血管模式[PVP]中的1种，并重复进行以评估可靠性。专家共识的人类真值[HGT]PVH PVP与LVDD分级（0至3 - 4级）相关，基于变压器的人工智能模型["PVPI"]的PVH分级预测也与之相关。尽管在PVP分配中存在阅片者内/阅片者间可靠性依赖经验的情况，但总体一致性显著（p < 0.001）。随着HGT PVH分期增加，发现LVDD分级有显著（p < 0.001）上升趋势；虽然PVH分期在0级/无LVDD时具有可信度支持，但直到3 - 4级/限制性充盈时才实现对LVDD分级的可靠识别。然而，PVPI PVH分级随LVDD分级的上升趋势在统计学上显著（p < 0.001）更强。尽管经过验证，但用于LVDD分级的PVH分期受阅片者变异性限制。人工智能辅助的PVH分级可能有助于更早、更广泛地对HF中普遍存在的LVDD进行客观的CXR筛查。
