ImportanceAortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization.ObjectiveTo deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression.Design, Setting, and ParticipantsThis is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024.ExposureDASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos.Main Outcomes and MeasuresAnnualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR).ResultsA total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors.Conclusions and RelevanceIn this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

中文翻译：

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基于多模态视频的 AI 生物标志物用于主动脉瓣狭窄的发展和进展

重要性主动脉瓣狭窄（AS）是一个重大的公共卫生挑战，其治疗前景不断扩大，但目前的生物标志物并不能为个性化筛查和随访提供信息。基于视频的人工智能 (AI) 生物标志物（数字 AS 严重性指数 [DASSi]）可以使用单视图长轴超声心动图检测严重 AS，无需多普勒表征。目标将 DASSi 部署到无 AS 或轻度或中度 AS 的患者设计、设置和参与者这是一项队列研究，检查了耶鲁纽黑文卫生系统 (YNHHS; 2015-2021) 和 Cedars-Sinai 医疗中心 (YNHHS; 2015-2021) 接受超声心动图检查的 2 组无严重 AS 的患者。华润上华；2018-2019）。英国生物银行进一步开发了一种新颖的计算管道，用于将 DASSi 跨模式转换为心脏磁共振 (CMR) 成像。分析于 2023 年 8 月至 2024 年 2 月期间进行。ExposureDASSi（范围，0-1）源自应用于超声心动图和 CMR 视频的 AI。主要结果和措施主动脉瓣流速峰值 (AV-V) 的年化变化最大限度）和晚期（>6 个月）主动脉瓣置换术 (AVR)。 结果 超声心动图研究共纳入 12 599 名参与者（YNHHS：n = 8798；中位 [IQR] 年龄，71 [60-80] 岁；4250 名 [48.3%] 女性；中位 [IQR] 随访时间，4.1 [2.4-5.4] 年；CSMC：n = 3801；中位 [IQR] 年龄，67 [54-78] 岁；1685 [44.3%]女性；中位 [IQR] 随访时间为 3.4 [2.8-3.9] 年）。较高的基线 DASSi 与 AV-V 的更快进展相关最大限度（每 0.1 DASSi 增量：YNHHS，5483 名参与者中每年 0.033 m/s [95% CI，0.028-0.038]；CSMC，1292 名参与者中每年 0.082 m/s [95% CI，0.053-0.111]），与小于 0.2 的值相比，0.2 或更大的值与高 4 至 5 倍的 AVR 风险相关（YNHHS：715 个事件；调整后的风险比 [HR]，4.97 [95% CI，2.71-5.82]；CSMC：56 个事件；调整后的 HR，4.04 [95% CI，0.92-17.70]），与年龄、性别、种族、民族、射血分数和 AV-V 无关最大限度。这一结果在 45 474 名接受 CMR 成像的参与者（中位 [IQR] 年龄，65 [59-71] 岁；23 559 [51.8%] 女性；中位 [IQR] 随访，2.5 [1.6-3.9] 年）中得到重现。英国生物银行（对于 DASSi ≥0.2 的参与者与 DASSi <.02 的参与者，调整后的 HR，11.38 [95% CI，2.56-50.57]）。显着性图和全表组关联研究支持心脏结构和功能以及传统心血管危险因素之间的关联。结论和相关性在这项对没有严重 AS 的患者进行超声心动图或 CMR 成像的队列研究中，一种新的基于 AI 的视频生物标志物与 AS 独立相关的发展和进展，实现心血管成像模式的机会性风险分层以及手持设备上的潜在应用。