Casula Matteo, Dusi Veronica, Camps Saskia, Gringet Jérémie, Benoit Tristan, Garonna Adriano, Rordorf Roberto
Arrhythmia and Electrophysiology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Section of Cardiology, Department of Molecular Medicine, University of Pavia, Pavia, Italy.
Front Cardiovasc Med. 2022 Apr 25;9:849234. doi: 10.3389/fcvm.2022.849234. eCollection 2022.
The management of the cardio-respiratory motion of the target and the reduction of the uncertainties related to patient's positioning are two of the main challenges that stereotactic arrhythmia radio-ablation (STAR) has to overcome. A prototype of a system was developed that can automatically acquire and interpret echocardiographic images using an artificial intelligence (AI) algorithm to calculate cardiac displacement in real-time.
We conducted a single center study enrolling consecutive patients with a history of ventricular arrhythmias (VA) in order to evaluate the feasibility of this automatic acquisition system. Echocardiographic images were automatically acquired from the parasternal and apical views with a dedicated probe. The system was designed to hold the probe fixed to the chest in the supine position during both free-breathing and short expiratory breath-hold sequences, to simulate STAR treatment. The primary endpoint was the percentage of patients reaching a score ≥2 in a multi-parametric assessment evaluating the quality of automatically acquired images. Moreover, we investigated the potential impact of clinical and demographic characteristics on achieving the primary endpoint.
We enrolled 24 patients (63 ± 14 years, 21% females). All of them had a history of VA and 21 (88%) had an ICD. Eight patients (33%) had coronary artery disease, 12 (50%) had non-ischemic cardiomyopathy, and 3 had idiopathic VA. Parasternal, as well as apical images were obtained from all patients except from one, in whom parasternal view could not be collected due to the patient's inability to maintain the supine position. The primary endpoint was achieved in 23 patients (96%) for the apical view, in 20 patients (87%) for the parasternal view, and in all patients in at least one of the two views. The images' quality was maximal (i.e., score = 4) in at least one of the two windows in 19 patients (79%). Atrial fibrillation arrhythmia was the only clinical characteristics associated with a poor score outcome in both imaging windows (apical = 0.022, parasternal = 0.014).
These results provide the proof-of-concept for the feasibility of an automatic ultrasonographic image acquisition system associated with an AI algorithm for real-time monitoring of cardiac motion in patients with a history of VA.
靶区心肺运动的管理以及减少与患者定位相关的不确定性是立体定向心律失常射频消融术（STAR）必须克服的两大主要挑战。已开发出一种系统原型，该系统可使用人工智能（AI）算法自动采集和解读超声心动图图像，以实时计算心脏位移。
我们开展了一项单中心研究，纳入了连续的有室性心律失常（VA）病史的患者，以评估这种自动采集系统的可行性。使用专用探头从胸骨旁和心尖视图自动采集超声心动图图像。该系统设计为在自由呼吸和短暂呼气屏气序列期间，将探头固定于仰卧位患者的胸部，以模拟STAR治疗。主要终点是在多参数评估中自动采集图像质量得分≥2分的患者百分比。此外，我们研究了临床和人口统计学特征对达到主要终点的潜在影响。
我们纳入了24例患者（年龄63±14岁，女性占21%）。所有患者均有VA病史，21例（88%）植入了植入式心律转复除颤器（ICD）。8例患者（33%）有冠状动脉疾病，12例（50%）有非缺血性心肌病，3例有特发性VA。除1例患者因无法维持仰卧位而未采集到胸骨旁视图外，所有患者均获得了胸骨旁和心尖图像。心尖视图有23例患者（96%）达到主要终点，胸骨旁视图有20例患者（87%）达到主要终点，且所有患者至少在两个视图中的一个达到主要终点。19例患者（79%）在两个窗口中的至少一个窗口图像质量达到最高（即得分=4）。心房颤动心律失常是两个成像窗口中与得分不佳结果相关的唯一临床特征（心尖=0.022，胸骨旁=0.014）。
这些结果为与AI算法相关的自动超声图像采集系统用于实时监测有VA病史患者心脏运动的可行性提供了概念验证。
